{"meta":{"query_hash":"97d5530e5994","filters":{"topic":"Statistical and Computational Modeling"},"cohort_total":88,"direct_labels_cover":0,"predictions_cover":88,"exported":88,"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/97d5530e5994","api":"https://metacan.xera.ac/api/v1/cohort?topic=Statistical+and+Computational+Modeling"},"results":[{"id":"W1499453370","doi":"10.5555/1227505.1227515","title":"Reinforcement Learning with Approximation Spaces","year":2006,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Statistical and Computational Modeling","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 Manitoba","funders":"","keywords":"Reinforcement learning; Computer science; Context (archaeology); Reinforcement; Testbed; Artificial intelligence; Set (abstract data type); Space (punctuation); Machine learning; Engineering; Geography","score_opus":0.010699586373205113,"score_gpt":0.21600292244230437,"score_spread":0.20530333606909926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499453370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011571407,0.000008740927,0.9632296,0.00034904605,0.0000467169,0.0001387389,5.0039e-7,0.00015548883,0.024499785],"genre_scores_gemma":[0.80136776,8.346701e-7,0.19792089,0.0002561613,0.000031376017,0.000024109839,0.000029044719,0.000003821547,0.00036600468],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895716,0.0000141813325,0.00029143042,0.00011949235,0.0004052864,0.0002124234],"domain_scores_gemma":[0.99954456,0.00007899568,0.000113402,0.00014621041,0.00006693719,0.00004990974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118679185,0.000108908054,0.00009905733,0.00007482338,0.00017756593,0.00036179702,0.00024091588,0.000020643178,0.00003971625],"category_scores_gemma":[0.000011332895,0.00008335957,0.000021971577,0.00020325588,0.000030441006,0.0009048261,0.000095868425,0.00009360667,0.00017701759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008853525,0.000024471885,0.00038942517,0.000036632773,0.000011499972,0.0000021190335,0.0007049811,0.32872242,0.000016019667,0.6585971,0.00041973652,0.011066738],"study_design_scores_gemma":[0.00032831,0.00013245111,0.00116893,0.000027991455,0.00000439618,0.0000122804295,0.00011307912,0.9818024,0.00014534146,0.013375322,0.0027436507,0.000145862],"about_ca_topic_score_codex":0.00004809687,"about_ca_topic_score_gemma":0.000002125806,"teacher_disagreement_score":0.78979635,"about_ca_system_score_codex":0.000049888902,"about_ca_system_score_gemma":0.000034770765,"threshold_uncertainty_score":0.3488817},"labels":[],"label_agreement":null},{"id":"W1522849611","doi":"10.1007/978-3-540-36562-4_36","title":"Acquisition of Vehicle Control Algorithms","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical and Computational Modeling","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":"Robustness (evolution); Computer science; Control engineering; Process (computing); MIMO; Controller (irrigation); Algorithm; Vehicle dynamics; Data acquisition; Engineering; Control theory (sociology); Control (management); Artificial intelligence; Automotive engineering","score_opus":0.018430925505061837,"score_gpt":0.23226976478684652,"score_spread":0.21383883928178468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522849611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.410911e-7,0.00010412543,0.7476958,0.00025160494,0.00011660287,0.000061244245,0.000012824877,0.000034267425,0.2517226],"genre_scores_gemma":[0.044203755,0.00003297739,0.6817011,0.005571619,0.00023143043,0.000011665187,0.00003068901,0.000041845884,0.2681749],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991109,0.000009371789,0.0002479484,0.00024302668,0.00028860598,0.00010018535],"domain_scores_gemma":[0.9993366,0.00014608563,0.00009571216,0.00019041038,0.00017938516,0.000051829284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007209189,0.00012315469,0.00019916988,0.000057511486,0.000029441848,0.000026440373,0.0002512947,0.00008932463,0.00020020726],"category_scores_gemma":[0.0000055816063,0.000109565226,0.00007536764,0.000020584419,0.000029630271,0.00007372283,0.00004163857,0.000087633525,0.00008644813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021308967,0.0000069200864,3.871796e-7,0.0000071264367,0.00001753462,0.0000047834938,0.000005871462,0.00046333898,0.000009523927,0.9725381,0.00049488316,0.02644941],"study_design_scores_gemma":[0.00020930928,0.00005781142,0.000012074447,0.000025452428,0.000009611237,0.0000049045184,3.3632318e-7,0.35892358,0.00002874361,0.637895,0.0027108104,0.00012236716],"about_ca_topic_score_codex":0.0000047612666,"about_ca_topic_score_gemma":2.5174188e-7,"teacher_disagreement_score":0.35846025,"about_ca_system_score_codex":0.000017759843,"about_ca_system_score_gemma":0.00003536987,"threshold_uncertainty_score":0.44679403},"labels":[],"label_agreement":null},{"id":"W1557643481","doi":"10.1002/9780470987605.ch10","title":"Improving Data Behaviour for Statistical Analysis: Ranking and Transformations","year":2008,"lang":"en","type":"other","venue":"","topic":"Statistical and Computational Modeling","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":"Geological Survey of Canada","funders":"","keywords":"Ranking (information retrieval); Sorting; Multivariate statistics; Transformation (genetics); Data set; Set (abstract data type); Computer science; Data transformation; Data mining; Mathematics; Statistics; Information retrieval; Algorithm; Artificial intelligence; Chemistry; Data warehouse","score_opus":0.04472685354965211,"score_gpt":0.2993347018271038,"score_spread":0.2546078482774517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557643481","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010794612,0.00023513877,0.9859109,0.00010627502,0.00007709891,0.000233134,0.000926634,0.0001833074,0.012326447],"genre_scores_gemma":[0.0027273847,0.000045121564,0.98481464,0.00013635284,0.00008209208,0.000028545406,0.00094549824,0.00005367186,0.011166681],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998823,0.000019831441,0.0002480951,0.0004975081,0.00022381106,0.00018775537],"domain_scores_gemma":[0.99908555,0.000280839,0.00007462315,0.00043187622,0.000041025673,0.00008606105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011086016,0.0001542997,0.00026389962,0.00026545828,0.00011300831,0.00013325748,0.00061909686,0.00008275593,0.00008600539],"category_scores_gemma":[0.000037473936,0.00013571556,0.000042582516,0.00023189037,0.00003679787,0.00017042563,0.00017072607,0.00008638805,0.000006535063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062192885,0.00008031213,0.00011218914,0.00015251718,0.0006667656,0.0000148831305,0.00019248342,0.0004367235,0.0000015505289,0.6595306,0.1268128,0.21199298],"study_design_scores_gemma":[0.00021948981,0.000017024344,0.00016150843,0.000013825639,0.0002965435,0.000007804858,0.0000060084294,0.98980945,3.8681304e-7,0.0039533013,0.005315868,0.0001987866],"about_ca_topic_score_codex":0.0003038965,"about_ca_topic_score_gemma":0.0001403349,"teacher_disagreement_score":0.98937273,"about_ca_system_score_codex":0.000009110898,"about_ca_system_score_gemma":0.00007113284,"threshold_uncertainty_score":0.553432},"labels":[],"label_agreement":null},{"id":"W1601420035","doi":"10.5539/ijsp.v4n3p88","title":"On the Detection of Heteroscedasticity by Using CUSUM Range Distribution","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical and Computational Modeling","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":"CUSUM; Heteroscedasticity; Mathematics; Statistics; Range (aeronautics); Econometrics; Distribution (mathematics); Homogeneity (statistics)","score_opus":0.04601178146693384,"score_gpt":0.28964086979726006,"score_spread":0.2436290883303262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601420035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17215273,0.000038778977,0.8268836,0.00033094265,0.00033221347,0.000042686923,0.00020319805,0.0000030094022,0.000012832672],"genre_scores_gemma":[0.96864146,0.0000056105628,0.031234588,0.000069945454,0.000039774044,8.1097255e-7,0.000004529708,0.0000019420916,0.0000013165978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99889815,0.000088213674,0.0003476249,0.00009849074,0.0004990963,0.0000684027],"domain_scores_gemma":[0.99799746,0.0005860632,0.00026307677,0.00006949209,0.0010157094,0.000068170215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005658413,0.00006460681,0.000104873165,0.000025974065,0.000040541923,0.0000664075,0.00026503755,0.000021951153,0.000002836833],"category_scores_gemma":[0.00086074654,0.000043696484,0.0000274288,0.00005915072,0.00007739678,0.00012865587,0.00006353464,0.000110413406,5.440528e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038450147,0.00039405507,0.002290423,0.000029575407,0.00012039931,0.000013964748,0.000387591,0.012710754,0.0018169846,0.8892828,0.00076223025,0.091806695],"study_design_scores_gemma":[0.0003119778,0.00018766236,0.002228806,0.000025475425,0.000008709647,0.00003213089,0.000009037899,0.4332891,0.00056371174,0.56322235,0.000068972004,0.000052058007],"about_ca_topic_score_codex":0.00003454797,"about_ca_topic_score_gemma":0.0000018494553,"teacher_disagreement_score":0.79648876,"about_ca_system_score_codex":0.0000815739,"about_ca_system_score_gemma":0.00006624012,"threshold_uncertainty_score":0.17818908},"labels":[],"label_agreement":null},{"id":"W1856862349","doi":"","title":"Distribution-free estimates of quantiles of the distribution of a contaminant in environmental media","year":2014,"lang":"en","type":"article","venue":"International Journal of Environment and Pollution","topic":"Statistical and Computational Modeling","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":"Ministry of the Environment, Conservation and Parks; University of Guelph","funders":"","keywords":"Quantile; Range (aeronautics); Environmental science; Statistics; Confidence interval; Distribution (mathematics); Snow; Limit (mathematics); Econometrics; Mathematics; Meteorology; Geography; Engineering","score_opus":0.007485438220865282,"score_gpt":0.21251248175817763,"score_spread":0.20502704353731235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1856862349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51788515,0.000112213,0.4813533,0.00038518576,0.00012676435,0.000025165136,0.00010563987,6.768445e-7,0.0000059191702],"genre_scores_gemma":[0.9969496,0.00009472585,0.002895136,0.0000113784145,0.000026813883,6.77105e-7,0.000018781458,0.0000015820036,0.0000013061552],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989156,0.000048275757,0.0004651537,0.00007458779,0.0004348402,0.000061509774],"domain_scores_gemma":[0.9992491,0.00017946398,0.00042624612,0.000090430476,0.000029902903,0.000024884628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002547647,0.00006156143,0.00013931292,0.000041783493,0.000017147831,0.000006909049,0.0003666323,0.000027137072,0.0000064577694],"category_scores_gemma":[0.00014394589,0.00004432112,0.00005962871,0.00003926991,0.0001471574,0.00013280421,0.0001355927,0.00006023044,3.324629e-7],"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":[0.00028214918,0.000883988,0.15249223,0.000044539753,0.00016163022,0.0000061689516,0.0010836176,0.033434175,0.0331287,0.687577,0.000115317074,0.0907905],"study_design_scores_gemma":[0.00077929295,0.000125232,0.8405471,0.00012110159,0.000017069087,0.000022411516,0.000039613806,0.10458163,0.007614447,0.045986027,0.0001078319,0.000058252903],"about_ca_topic_score_codex":0.00002234322,"about_ca_topic_score_gemma":0.0000018859979,"teacher_disagreement_score":0.68805486,"about_ca_system_score_codex":0.00005286341,"about_ca_system_score_gemma":0.000015037003,"threshold_uncertainty_score":0.18073629},"labels":[],"label_agreement":null},{"id":"W185698933","doi":"10.1007/978-3-642-01530-4_1","title":"Hybrid Computational Intelligence and GMDH Systems","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Statistical and Computational Modeling","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":"Minnow Environmental (Canada)","funders":"","keywords":"Group method of data handling; Computer science; Computational intelligence; Artificial intelligence; Machine learning; Univariate; Multivariate statistics","score_opus":0.12217266656649685,"score_gpt":0.3444799562249978,"score_spread":0.22230728965850094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W185698933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014001654,0.011986571,0.9628256,0.00073448493,0.0010088799,0.00047961072,0.00006006672,0.00017163559,0.022719173],"genre_scores_gemma":[0.45308933,0.0036608577,0.51357156,0.0021086873,0.00075251487,0.00011510722,0.00025430368,0.0001268423,0.026320796],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953901,0.000082166574,0.0014512014,0.0013805672,0.0011982727,0.00049767643],"domain_scores_gemma":[0.9947518,0.003171898,0.0004376579,0.00036892988,0.0010780155,0.00019169613],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005292612,0.0007175014,0.0009015082,0.00056079327,0.00030549703,0.00027867057,0.0010627469,0.00017427294,0.000022833],"category_scores_gemma":[0.00021559383,0.0007309959,0.00014317082,0.0002023578,0.00062535977,0.00037127468,0.0007379241,0.0007037841,0.00018481625],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006667159,0.000021264572,0.0000023338202,0.00006475647,0.000064005995,0.00009067559,0.00019844594,0.44885394,2.4793257e-8,0.48497233,0.0002197641,0.06550582],"study_design_scores_gemma":[0.00004419003,0.00009020718,0.00003192791,0.00042302845,0.000012126304,0.00013768286,0.000039668634,0.4789467,9.203217e-7,0.51912266,0.00075778336,0.0003931346],"about_ca_topic_score_codex":0.00001692554,"about_ca_topic_score_gemma":0.0000045113443,"teacher_disagreement_score":0.45307532,"about_ca_system_score_codex":0.0003218294,"about_ca_system_score_gemma":0.0002711109,"threshold_uncertainty_score":0.9995141},"labels":[],"label_agreement":null},{"id":"W196019468","doi":"10.1007/978-3-642-16373-9","title":"Conceptual Modeling – ER 2010","year":2010,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Statistical and Computational Modeling","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":"University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Computer science","score_opus":0.027273939598829258,"score_gpt":0.2609919015891631,"score_spread":0.23371796199033382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W196019468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012199646,0.0002444796,0.99271506,0.0006707349,0.0041994527,0.00025299244,0.00000836222,0.00021188956,0.0015750186],"genre_scores_gemma":[0.076677464,0.000009069165,0.91917264,0.0022687952,0.0011025756,0.000015229687,0.000015013972,0.000034945337,0.0007042643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954208,0.00004686649,0.00061002513,0.0017231874,0.0013688534,0.0008302574],"domain_scores_gemma":[0.99714714,0.0008364549,0.0001765858,0.0010958548,0.00046832627,0.0002756544],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070035167,0.00052284444,0.0005225833,0.00058703736,0.00032578534,0.00065712054,0.0039445343,0.00044900624,0.000030793588],"category_scores_gemma":[0.00020133718,0.00047485673,0.00013111281,0.00080048246,0.00083204516,0.0006416639,0.0014963977,0.0017724765,0.00012189301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003131115,0.000029765311,0.0000052324835,0.000012557835,0.0000063740877,0.000054778597,0.0005540034,0.6332378,0.00005995202,0.08201981,0.00011735789,0.28389925],"study_design_scores_gemma":[0.00013235706,0.000044143213,0.0000069192606,0.00008358608,0.000003453147,0.000033838223,9.773744e-8,0.64918876,0.000056018464,0.34974974,0.0003149624,0.00038611912],"about_ca_topic_score_codex":0.000033096814,"about_ca_topic_score_gemma":0.000042316464,"teacher_disagreement_score":0.28351313,"about_ca_system_score_codex":0.00024375573,"about_ca_system_score_gemma":0.001932507,"threshold_uncertainty_score":0.9997703},"labels":[],"label_agreement":null},{"id":"W200330463","doi":"10.1007/978-3-642-01530-4_4","title":"Hybrid Differential Evolution and GMDH Systems","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Statistical and Computational Modeling","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":"Minnow Environmental (Canada)","funders":"","keywords":"Differential evolution; Computer science; Generalization; Group method of data handling; Parametric statistics; Process (computing); Artificial intelligence; Machine learning; Mathematics","score_opus":0.08042657573527412,"score_gpt":0.323760208345076,"score_spread":0.2433336326098019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W200330463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000040034523,0.008068532,0.97691745,0.00026427055,0.0009943224,0.0002548398,0.000024458332,0.000091123,0.013344956],"genre_scores_gemma":[0.9015081,0.0012088056,0.07645799,0.0002955116,0.00055559166,0.00004185569,0.00006719381,0.000041385658,0.019823546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99761754,0.00003980396,0.0006978223,0.0007519451,0.000628741,0.00026417253],"domain_scores_gemma":[0.9980497,0.0009916233,0.00020962753,0.00020663267,0.00045883888,0.00008361602],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017330906,0.00037249172,0.0004962656,0.00028892444,0.0001702671,0.00012844005,0.0004773412,0.00009646209,0.0000087924045],"category_scores_gemma":[0.00008458192,0.00036330216,0.000074841504,0.00007347433,0.00026204696,0.00018200693,0.0004034669,0.0003510069,0.000057056703],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005816267,0.000014863708,0.0000025109757,0.000054606066,0.000051830397,0.00004368272,0.000111669404,0.15442377,1.5654946e-7,0.8016131,0.00016803417,0.043509956],"study_design_scores_gemma":[0.000042901644,0.000052327476,0.00007980033,0.00026095659,0.000008740447,0.000046210585,0.000013271176,0.45474938,4.979948e-7,0.5442952,0.00023955927,0.0002111751],"about_ca_topic_score_codex":0.000012522823,"about_ca_topic_score_gemma":0.0000028991594,"teacher_disagreement_score":0.9014681,"about_ca_system_score_codex":0.00025478762,"about_ca_system_score_gemma":0.00010512239,"threshold_uncertainty_score":0.9998819},"labels":[],"label_agreement":null},{"id":"W2015078574","doi":"10.5539/ass.v11n8p277","title":"Application of Methods of Optimal Planning in the Meat and Dairy Subsector of the Agribusiness","year":2015,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Statistical and Computational Modeling","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":"Agribusiness; Business; Plan (archaeology); Dairy industry; Agriculture; Refrigeration; Environmental economics; Production (economics); Agricultural science; Agricultural engineering; Agricultural economics; Industrial organization; Economics; Microeconomics; Environmental science; Food science","score_opus":0.06009357223246754,"score_gpt":0.3684224666340693,"score_spread":0.30832889440160177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015078574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09172553,0.00003140662,0.9059403,0.0003574963,0.00004373552,0.0000686127,0.000001345535,0.0000030002013,0.0018286],"genre_scores_gemma":[0.9175411,1.7933431e-7,0.08241307,0.000027503464,0.000013874811,0.0000026841242,1.332341e-7,7.629087e-7,7.032629e-7],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99924827,0.0000868314,0.00014715333,0.00012013408,0.0003143935,0.000083248364],"domain_scores_gemma":[0.9994908,0.00013766569,0.000101813865,0.00010274632,0.00014785182,0.000019151345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096681254,0.000034921566,0.00008054255,0.00003133946,0.00007163066,0.000016499249,0.000654161,0.000015725622,1.5917499e-7],"category_scores_gemma":[0.00014135116,0.000020914655,0.000014349158,0.00089932425,0.0003897335,0.00013844884,0.00014032866,0.000042618874,8.304643e-8],"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":[0.000007827543,0.000046432,0.0038217844,0.00001574739,0.0000022509812,2.5764365e-7,0.019604491,0.001657872,0.0052752495,0.66999257,0.00001146079,0.29956406],"study_design_scores_gemma":[0.00023208323,0.000035011268,0.6815413,0.000021956388,0.000004988851,0.0000038570993,0.001954573,0.24566442,0.0036540271,0.066787474,0.000023837316,0.000076472825],"about_ca_topic_score_codex":0.00007546009,"about_ca_topic_score_gemma":0.0000017711573,"teacher_disagreement_score":0.82581556,"about_ca_system_score_codex":0.000010238697,"about_ca_system_score_gemma":0.00009793344,"threshold_uncertainty_score":0.14359893},"labels":[],"label_agreement":null},{"id":"W2022045699","doi":"10.1016/j.inffus.2013.10.009","title":"Addendum for “The TBM global distance measure for the association of uncertain combat ID declarations”","year":2013,"lang":"en","type":"article","venue":"Information Fusion","topic":"Statistical and Computational Modeling","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 Laval; Thales (Canada)","funders":"","keywords":"Addendum; Measure (data warehouse); Computer science; Association (psychology); Data mining; Law; Psychology","score_opus":0.022112517882283755,"score_gpt":0.25524714110726404,"score_spread":0.2331346232249803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022045699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004079666,0.000070651724,0.9922961,0.005613086,0.00039801295,0.0008258993,0.000086360444,0.000034653956,0.00026726167],"genre_scores_gemma":[0.95024866,0.0000098374085,0.04793805,0.0010922609,0.00009867391,0.00043434565,0.000090208036,0.0000029040461,0.00008504844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990234,0.000023780261,0.00035137034,0.00007854362,0.00038564866,0.00013725577],"domain_scores_gemma":[0.99749976,0.0012378664,0.00026210264,0.00017023907,0.0008024033,0.000027613956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000413975,0.000074806936,0.000081190236,0.000020624753,0.00039693597,0.00019591143,0.00038364317,0.000047369293,0.00001075255],"category_scores_gemma":[0.00043727172,0.000044980017,0.000059124533,0.00020828881,0.000018717446,0.0009245814,0.000055782097,0.00004662971,0.000026979678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025396805,0.000021714282,0.00023907718,0.00003398319,0.000028492599,1.41883705e-8,0.0006112611,0.037781607,0.000028334083,0.65415275,0.03156095,0.27551645],"study_design_scores_gemma":[0.00034814645,0.000034951336,0.0043856,0.0000125852175,0.000011239583,5.3119163e-7,0.00009992117,0.9389187,0.000032796706,0.040686637,0.0154068135,0.00006207074],"about_ca_topic_score_codex":0.0000720613,"about_ca_topic_score_gemma":0.000025828056,"teacher_disagreement_score":0.9498407,"about_ca_system_score_codex":0.00009978358,"about_ca_system_score_gemma":0.0000721319,"threshold_uncertainty_score":0.30529505},"labels":[],"label_agreement":null},{"id":"W2030381164","doi":"10.1002/sim.1106","title":"Prediction trees with soft nodes for binary outcomes","year":2002,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical and Computational Modeling","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":"Montreal Heart Institute; McGill University","funders":"","keywords":"Computer science; Event (particle physics); Novelty; Tree (set theory); Data mining; Data set; Set (abstract data type); Node (physics); Algorithm; Statistics; Artificial intelligence; Mathematics","score_opus":0.04925586001394029,"score_gpt":0.2959766245404736,"score_spread":0.2467207645265333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030381164","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083002617,0.00010885828,0.99607694,0.0020404407,0.0002362318,0.0001658013,0.000096111675,0.00006600656,0.0003795868],"genre_scores_gemma":[0.41293287,0.000017128505,0.5861564,0.00047662685,0.00008132039,0.00004203841,0.000030074712,0.000008457889,0.00025503227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891084,0.00002349811,0.00027227358,0.00026467102,0.00032834616,0.00020038038],"domain_scores_gemma":[0.9984643,0.0011264074,0.00005587038,0.00016320351,0.00012188274,0.00006836975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015761875,0.00011849554,0.00020448108,0.00011797945,0.00007792567,0.000020369875,0.0002283018,0.00002762916,0.000046862977],"category_scores_gemma":[0.00031995855,0.00008183411,0.000011749742,0.00020493468,0.00009988023,0.00010332303,0.00003246995,0.000090337664,0.000008163869],"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":[0.0000339397,0.0001625066,0.011421115,0.00007226567,0.00003724507,0.00007130125,0.0014860645,0.016104681,0.000029963274,0.8376168,0.013059288,0.119904846],"study_design_scores_gemma":[0.00094167184,0.0003895937,0.023456441,0.00005747445,0.000011057644,0.0000068766417,0.00004398668,0.85482365,0.000001657752,0.119902745,0.0002758002,0.000089071145],"about_ca_topic_score_codex":0.000026030337,"about_ca_topic_score_gemma":0.000021843392,"teacher_disagreement_score":0.83871895,"about_ca_system_score_codex":0.000030038815,"about_ca_system_score_gemma":0.000015843685,"threshold_uncertainty_score":0.3337098},"labels":[],"label_agreement":null},{"id":"W2036293463","doi":"10.1117/12.2049575","title":"Improving the efficiency of nonparametric entropy estimation","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Statistical and Computational Modeling","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":"Wilfrid Laurier University","funders":"","keywords":"Estimator; Nonparametric statistics; Ergodic theory; Entropy (arrow of time); Mathematics; Mathematical optimization; Entropy estimation; Applied mathematics; Metric (unit); Metric space; Computer science; Statistics; Discrete mathematics; Mathematical analysis; Engineering","score_opus":0.00927582075876395,"score_gpt":0.22275859226920333,"score_spread":0.21348277151043937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036293463","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7753324,0.00004280414,0.22234128,0.001321462,0.0001890991,0.00026988835,0.000008069629,0.000051822346,0.00044317075],"genre_scores_gemma":[0.60614395,0.000007326064,0.39358437,0.00007285966,0.00011734859,0.00003924905,0.0000016961009,0.00001400248,0.000019228577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776584,3.4203012e-8,0.000697513,0.00035397834,0.0008761078,0.00030653825],"domain_scores_gemma":[0.99708086,0.00067045225,0.00048012703,0.000082576254,0.0016045851,0.000081424914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008841026,0.00021754904,0.00030548227,0.000117462914,0.00011205452,0.00014364134,0.0016434789,0.000093721836,0.0000029799173],"category_scores_gemma":[0.001654653,0.0001514566,0.0003822733,0.0006462692,0.00019044505,0.00048302568,0.00027551365,0.00022408906,0.0000015876111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017427112,0.000078825644,0.00006164781,0.00020221686,0.0000783996,1.7123073e-8,0.00014106541,0.004106448,0.10018993,0.89062554,0.00019906748,0.0042993934],"study_design_scores_gemma":[0.00040363253,0.00021350975,0.00042383056,0.0000916972,0.00004703456,0.0000064221117,0.00010560974,0.94211763,0.038383085,0.017940942,0.00010956515,0.00015702177],"about_ca_topic_score_codex":0.000013982648,"about_ca_topic_score_gemma":3.1522124e-8,"teacher_disagreement_score":0.9380112,"about_ca_system_score_codex":0.00007401231,"about_ca_system_score_gemma":0.000038219292,"threshold_uncertainty_score":0.6176221},"labels":[],"label_agreement":null},{"id":"W2051855544","doi":"10.1080/03081079.2013.798902","title":"Optimization and analysis of decision trees and rules: dynamic programming approach","year":2013,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Statistical and Computational Modeling","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":"Carleton University","funders":"","keywords":"Decision tree; Computer science; Dagger; Dynamic programming; Software; Machine learning; Mathematical optimization; Artificial intelligence; Mathematics; Algorithm; Programming language","score_opus":0.011729313364343684,"score_gpt":0.26323630115752766,"score_spread":0.251506987793184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051855544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1856688,0.0003996486,0.8135489,0.000090084126,0.00019902966,0.000053831314,0.00000375612,0.000004937652,0.000030967934],"genre_scores_gemma":[0.6284797,0.000029113653,0.37142125,0.000012554349,0.00003782878,0.000002512371,0.0000049076616,0.0000020978575,0.000010009418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988387,0.00004108413,0.0004679322,0.00012776173,0.00045338346,0.00007108684],"domain_scores_gemma":[0.9988223,0.00011541138,0.00030694678,0.000059898073,0.0006296134,0.000065843364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020813574,0.00007050968,0.00020224339,0.0003652448,0.000028040222,0.00024024263,0.00028558736,0.000028265411,0.000002998343],"category_scores_gemma":[0.000041530675,0.00005372154,0.000056789617,0.00019310204,0.000027490118,0.00037416676,0.00007831678,0.000049436305,4.3047655e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009368749,0.00004731386,0.0020602692,0.000008531447,0.0004774953,0.0000050017998,0.00020988626,0.8619411,0.00017523041,0.016724916,0.000017204424,0.11832368],"study_design_scores_gemma":[0.00020145041,0.00004380353,0.008997308,0.000035205027,0.000047434965,0.0000912795,0.000045035868,0.98858935,0.000003747707,0.0018779952,0.000013606007,0.000053778895],"about_ca_topic_score_codex":0.00008557094,"about_ca_topic_score_gemma":0.0000013414904,"teacher_disagreement_score":0.44281092,"about_ca_system_score_codex":0.000029003812,"about_ca_system_score_gemma":0.000021230044,"threshold_uncertainty_score":0.23166652},"labels":[],"label_agreement":null},{"id":"W2144374444","doi":"10.1017/s002185961000105x","title":"Predicting carcass energy content and composition in broilers using the group method of data handling-type neural networks","year":2010,"lang":"en","type":"article","venue":"The Journal of Agricultural Science","topic":"Statistical and Computational Modeling","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 Guelph","funders":"","keywords":"Abdominal fat; Carcass weight; Food science; Animal science; Body weight; Biology; Endocrinology","score_opus":0.07078887990233552,"score_gpt":0.3046219600000181,"score_spread":0.2338330800976826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144374444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5209207,0.00008518534,0.47830662,0.00043291177,0.00022473157,0.000023268789,5.988295e-7,0.0000022197428,0.0000037587404],"genre_scores_gemma":[0.9412636,0.000012413106,0.058540817,0.00008480779,0.000095903204,1.3576019e-7,5.9884906e-7,0.0000010488357,6.4540865e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886525,0.0001259927,0.00030309582,0.0001311898,0.00041596845,0.00015847683],"domain_scores_gemma":[0.9986077,0.000594477,0.0002718727,0.0001572189,0.00030762958,0.00006111507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017206124,0.00006826151,0.0001162697,0.000038673403,0.00025766977,0.00011638562,0.0013220717,0.000018460743,4.5844294e-7],"category_scores_gemma":[0.00009307163,0.000028082579,0.00001817782,0.0005652,0.00022486226,0.00082233414,0.00041198166,0.0002536925,2.4337409e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043718468,0.00004376798,0.002600936,0.000005356395,0.000016397104,0.000005640125,0.0011019272,0.56060153,0.3745437,0.020246413,0.000013634193,0.04077698],"study_design_scores_gemma":[0.0001071798,0.00005801832,0.075307906,0.000026563339,0.00001144319,0.0004764507,0.00014243652,0.92267597,0.00046546702,0.0006850742,8.895933e-7,0.000042620326],"about_ca_topic_score_codex":0.00034157254,"about_ca_topic_score_gemma":0.000066480905,"teacher_disagreement_score":0.42034292,"about_ca_system_score_codex":0.000018161783,"about_ca_system_score_gemma":0.000037872287,"threshold_uncertainty_score":0.245676},"labels":[],"label_agreement":null},{"id":"W2174445735","doi":"10.1515/jaiscr-2015-0013","title":"A Parametric Testing Of The Firefly Algorithm In The Determination Of The Optimal Osmotic Drying Parameters Of Mushrooms","year":2014,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Statistical and Computational Modeling","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":"","keywords":"Firefly algorithm; Parametric statistics; Mathematical optimization; Firefly protocol; Osmotic dehydration; Process (computing); Sensitivity (control systems); Range (aeronautics); Computer science; Linear programming; Algorithm; Mathematics; Dehydration; Chemistry; Engineering; Particle swarm optimization; Statistics","score_opus":0.16684332536096647,"score_gpt":0.3807191131399495,"score_spread":0.21387578777898303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2174445735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4487,0.000040139214,0.55088675,0.00020403812,0.00008429785,0.000072577146,3.5474577e-7,0.0000013434269,0.000010500868],"genre_scores_gemma":[0.8835872,0.0000033232516,0.11633384,0.000028309483,0.00004264949,6.2441495e-7,4.340068e-8,0.0000031369386,9.2040796e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971198,0.0006396987,0.00086282514,0.0001490487,0.0010046073,0.00022404426],"domain_scores_gemma":[0.98980504,0.008624994,0.0005066269,0.00022234175,0.0008052682,0.000035744622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005036526,0.00008106714,0.00021887678,0.00025362367,0.00021034305,0.000083409286,0.0011624901,0.000040707917,5.6548623e-7],"category_scores_gemma":[0.0048721456,0.000043523232,0.000094054645,0.0017418533,0.00034459893,0.00011753069,0.00030661386,0.00048456134,3.217711e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009736293,0.000092448514,0.0013544515,0.00003871254,0.0000065298927,0.0000020263726,0.0026534651,0.09919913,0.0003884739,0.0036847722,0.0000016498828,0.8925686],"study_design_scores_gemma":[0.000034373785,0.00024033342,0.0050966446,0.00026106575,0.0000057903612,0.000025325855,0.00058390986,0.9278484,0.0046331156,0.061229195,7.912485e-7,0.00004102804],"about_ca_topic_score_codex":0.0001030168,"about_ca_topic_score_gemma":0.0000035773517,"teacher_disagreement_score":0.8925276,"about_ca_system_score_codex":0.00002444955,"about_ca_system_score_gemma":0.00013434491,"threshold_uncertainty_score":0.5832765},"labels":[],"label_agreement":null},{"id":"W2282753672","doi":"10.14288/1.0094602","title":"Analytical procedures for reducing uncertainty in the technological control of eutrophication","year":2010,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Statistical and Computational Modeling","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":"Risk analysis (engineering); Control (management); Eutrophication; Environmental science; Environmental resource management; Natural resource economics; Business; Environmental economics; Environmental planning; Computer science; Economics; Ecology; Artificial intelligence; Biology","score_opus":0.011664415424277506,"score_gpt":0.2083846767356591,"score_spread":0.1967202613113816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282753672","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5455096,0.000008745616,0.4535652,0.00066885666,0.000018455325,0.00013749061,0.00001623825,0.000015782967,0.00005966365],"genre_scores_gemma":[0.9828871,0.0000027108395,0.017041596,0.000045148186,0.0000098989885,0.0000016275169,0.0000027728736,0.00000158783,0.0000075682674],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993754,0.000028629946,0.000110129295,0.0002098109,0.00016254633,0.0001135101],"domain_scores_gemma":[0.9992627,0.00030275644,0.00006948927,0.00016009565,0.00017938801,0.000025590432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028423851,0.000021614173,0.00012403923,0.00003236529,0.00008582248,0.000040713003,0.0005655271,0.00006308853,0.0000037788616],"category_scores_gemma":[0.00023989766,0.00004753165,0.000050718274,0.0002649048,0.0002163084,0.00010963001,0.00005023339,0.00012529097,6.3754885e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025028232,0.0003270004,0.0028743853,0.00009039014,0.000021667682,0.000023509285,0.00025874694,0.004016098,0.0011511729,0.03847407,0.00027027694,0.9524677],"study_design_scores_gemma":[0.00042241736,0.000054013333,0.51305115,0.000027039276,0.000007783119,0.00001712347,0.0001339673,0.44719106,6.1799716e-7,0.03902983,0.000014175603,0.00005084495],"about_ca_topic_score_codex":0.003828667,"about_ca_topic_score_gemma":0.0096885245,"teacher_disagreement_score":0.9524168,"about_ca_system_score_codex":0.000010972429,"about_ca_system_score_gemma":0.00007221207,"threshold_uncertainty_score":0.5787825},"labels":[],"label_agreement":null},{"id":"W2399886942","doi":"","title":"Clustering of Products to Identify Optimal Inventory Prediction Models.","year":2011,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Statistical and Computational Modeling","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":"Cluster analysis; Computer science; Data mining; Artificial intelligence","score_opus":0.2398035938578342,"score_gpt":0.3504457422213326,"score_spread":0.11064214836349842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399886942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02342448,0.0000048892384,0.96755034,0.0006108811,0.0011944093,0.0001787414,0.000027740847,0.000075879994,0.006932639],"genre_scores_gemma":[0.90699935,0.000005647943,0.092598885,0.00019439802,0.00010425535,0.00002879545,0.000007845114,0.000008048532,0.00005275791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981598,0.00004388811,0.0005360361,0.00048506603,0.00055555144,0.00021966064],"domain_scores_gemma":[0.998743,0.000048152957,0.00013678538,0.0002795119,0.00065579626,0.00013673533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024094137,0.00015086311,0.00014232824,0.0003291464,0.0000747258,0.00011820558,0.0010891951,0.000055250486,0.0001626236],"category_scores_gemma":[0.00014411802,0.00015706675,0.000049656675,0.00031811377,0.00007379906,0.0005105843,0.00024324513,0.0001576434,0.0001999076],"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":[0.00006621832,0.00014068931,0.000030338642,0.000010251058,0.000021683452,0.000014685741,0.0039582537,0.04035263,0.0010090902,0.89040434,0.00003303399,0.06395879],"study_design_scores_gemma":[0.000017461387,0.00014521656,0.00037499366,0.0000771085,0.0000023770313,0.0000065518616,0.0001733834,0.75312114,0.010803147,0.23514438,0.000010232712,0.00012403615],"about_ca_topic_score_codex":0.00011807448,"about_ca_topic_score_gemma":0.000020762547,"teacher_disagreement_score":0.8835749,"about_ca_system_score_codex":0.00006643185,"about_ca_system_score_gemma":0.0001299714,"threshold_uncertainty_score":0.6404996},"labels":[],"label_agreement":null},{"id":"W2405866979","doi":"","title":"Criteria of Human Software Evaluation: Feature Selection Approach.","year":2011,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Statistical and Computational Modeling","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; Feature selection; Selection (genetic algorithm); Software; Feature (linguistics); Artificial intelligence; Data mining; Programming language","score_opus":0.033624357412264334,"score_gpt":0.2584092899213742,"score_spread":0.22478493250910986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405866979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019221712,0.0009456063,0.9787323,0.0000038496523,0.00033775903,0.0001240022,0.0000035204423,0.0005048078,0.00012643327],"genre_scores_gemma":[0.51398456,0.0000024747353,0.48585272,0.0000029213327,0.00008148638,0.000027831238,0.0000066135703,0.000017364377,0.000024015279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989501,0.000020583173,0.0002320692,0.00034083385,0.00020931047,0.00024710593],"domain_scores_gemma":[0.99930584,0.00012709103,0.000043239317,0.00018171871,0.00022737622,0.00011471763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028151736,0.00021240633,0.00022221587,0.00020017353,0.00008078968,0.00004334895,0.00025375976,0.00009455104,0.000011570244],"category_scores_gemma":[0.00028878535,0.00022036511,0.00005601129,0.0003903936,0.000015249522,0.00024249507,0.00011976927,0.00019729862,0.0000031278676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033181295,0.00075981417,0.006058838,0.0039129998,0.00056970323,0.00002177527,0.019041091,0.44801086,0.010487908,0.31870598,0.00110417,0.19129367],"study_design_scores_gemma":[0.0002431975,0.000067527915,0.0073212353,0.00010406373,0.00002354936,0.000028382936,0.000009951947,0.9900587,0.0009187328,0.00084799086,0.00010430634,0.0002723287],"about_ca_topic_score_codex":0.000006779799,"about_ca_topic_score_gemma":4.6268116e-7,"teacher_disagreement_score":0.54204786,"about_ca_system_score_codex":0.000042850927,"about_ca_system_score_gemma":0.000043299642,"threshold_uncertainty_score":0.8986228},"labels":[],"label_agreement":null},{"id":"W2491942999","doi":"","title":"Improving Computational Efficiency in Modeling Complex Environmental Systems (with Uncertainty) I Posters","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Statistical and Computational Modeling","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","score_opus":0.015350280245402118,"score_gpt":0.2159183163510405,"score_spread":0.2005680361056384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491942999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24446842,0.00004767151,0.7545923,0.000102337835,0.00010675656,0.00014071165,0.0000033748972,0.00007941544,0.0004589991],"genre_scores_gemma":[0.8732687,8.389023e-7,0.12640421,0.00020384435,0.0000634451,0.000015279074,0.000021806907,0.0000143994985,0.000007475991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978532,0.00014844746,0.00045890608,0.00056592317,0.00054433855,0.00042916616],"domain_scores_gemma":[0.99901026,0.00046740595,0.00013900327,0.00021249235,0.000052015275,0.00011881441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005521066,0.00021123834,0.00023961595,0.00015875872,0.0002060742,0.00021591593,0.0005096165,0.00004849791,0.0000016673503],"category_scores_gemma":[0.000055763703,0.00018541398,0.000041131214,0.00017589664,0.000046944395,0.00024572923,0.0001950956,0.00017222737,0.00004211511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008852816,0.000039973776,0.0026497566,0.000023384715,0.0000053581925,0.000005940018,0.0002289821,0.976157,0.0001657499,0.015486442,0.0000070420615,0.0052214805],"study_design_scores_gemma":[0.00052744144,0.000088202425,0.0009526894,0.000089738445,0.0000044080707,0.000022246126,0.00013420115,0.9955445,0.0000016330712,0.0023554456,0.000017017814,0.00026248442],"about_ca_topic_score_codex":0.0013031714,"about_ca_topic_score_gemma":0.0000455869,"teacher_disagreement_score":0.6288003,"about_ca_system_score_codex":0.0001157262,"about_ca_system_score_gemma":0.000046867004,"threshold_uncertainty_score":0.75609624},"labels":[],"label_agreement":null},{"id":"W2493748416","doi":"10.1142/p736","title":"GMDH-Methodology and Implementation in C","year":2011,"lang":"en","type":"book","venue":"IMPERIAL COLLEGE PRESS eBooks","topic":"Statistical and Computational Modeling","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":"Sheridan College","funders":"","keywords":"Computer science","score_opus":0.09846748208532548,"score_gpt":0.3348498855477052,"score_spread":0.2363824034623797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493748416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036081075,0.0005437224,0.5678721,0.000048881448,0.0015351629,0.0009947057,0.00022619378,0.00013614641,0.42828226],"genre_scores_gemma":[0.022274658,0.000115333365,0.61701715,0.0010312366,0.0015534331,0.0005999319,0.0001199275,0.00013475853,0.35715353],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983066,0.00020470281,0.00045041132,0.00054452545,0.00021549083,0.00027829577],"domain_scores_gemma":[0.9989445,0.00048432578,0.00014372225,0.00024640915,0.000085925494,0.00009510625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003297643,0.0002431099,0.0003743615,0.00014893002,0.00007376589,0.00008116101,0.00045889852,0.00021538192,0.000023787343],"category_scores_gemma":[0.00003431378,0.00024532602,0.000047297362,0.000017801678,0.000077079894,0.00009688313,0.00046619543,0.00024305235,0.0000049988816],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040072886,0.0000064043024,0.0000010108859,0.000035020355,0.00001786536,0.000038340175,0.00082988467,0.0000074952327,0.000041537995,0.95921147,0.0013962144,0.038374674],"study_design_scores_gemma":[0.0011678569,0.00018230572,0.00007092097,0.00006037113,0.000023325994,0.00004333223,0.000013167465,0.008202839,0.00029939116,0.95106786,0.03840866,0.0004599637],"about_ca_topic_score_codex":0.00033002492,"about_ca_topic_score_gemma":0.000103845065,"teacher_disagreement_score":0.07112872,"about_ca_system_score_codex":0.00006995394,"about_ca_system_score_gemma":0.00034780847,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W2543628422","doi":"10.1109/vnis.1995.518858","title":"Multivariate calibration of single regime speed-flow-density relationships [road traffic management]","year":2002,"lang":"en","type":"article","venue":"","topic":"Statistical and Computational Modeling","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":"Queen's University","funders":"","keywords":"Multivariate statistics; Calibration; Road traffic; Traffic flow (computer networking); Computer science; Flow (mathematics); Transport engineering; Statistics; Computer network; Mathematics; Engineering; Machine learning","score_opus":0.07146158413245036,"score_gpt":0.24444116700219798,"score_spread":0.17297958286974763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543628422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016874494,0.000023688237,0.97277004,0.00073089974,0.00011475762,0.00010597864,0.0000018539384,0.00014262095,0.009235649],"genre_scores_gemma":[0.70413905,0.0000021394992,0.2949255,0.00007916498,0.00001905827,0.0000012484048,0.000003679101,0.000003668432,0.00082645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989829,0.00007460762,0.00027090026,0.00026447736,0.000267603,0.00013951503],"domain_scores_gemma":[0.9994056,0.00015566642,0.00007340373,0.0002278491,0.00007761335,0.00005986857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013614685,0.00008936134,0.00010817361,0.000085605396,0.00011356884,0.000062136554,0.00024579358,0.000038406528,0.00007353633],"category_scores_gemma":[0.000043559292,0.00008292473,0.00004146628,0.0003135032,0.00002786811,0.0003567352,0.00009125694,0.00007886641,0.00006419797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007567632,0.00031561896,0.00006439635,0.000028064042,0.000034883826,0.000017239654,0.00060199975,0.16807692,0.00031073033,0.72628194,0.0016384613,0.10262218],"study_design_scores_gemma":[0.00022591255,0.000029202567,0.0022110913,0.0000134373195,0.0000075676276,0.000004346665,0.000019652798,0.9784366,0.00013556959,0.018776016,0.00004003559,0.000100597616],"about_ca_topic_score_codex":0.000015506525,"about_ca_topic_score_gemma":0.0000039858087,"teacher_disagreement_score":0.81035966,"about_ca_system_score_codex":0.000021128075,"about_ca_system_score_gemma":0.0000051787083,"threshold_uncertainty_score":0.33815724},"labels":[],"label_agreement":null},{"id":"W2546844340","doi":"10.21101/hygiena.a1204","title":"Software for Toxicological Data Searches (toXie)","year":2014,"lang":"en","type":"article","venue":"Hygiena","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"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 de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"","keywords":"Physics","score_opus":0.15444038945708083,"score_gpt":0.33369462985001264,"score_spread":0.1792542403929318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546844340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001450466,0.00002329096,0.99665797,0.0011063275,0.00013526966,0.00009159318,0.000020912417,0.00013416678,0.0003799793],"genre_scores_gemma":[0.36423367,7.9428804e-7,0.6348364,0.00069565343,0.00010653536,0.000013528686,0.000029901701,0.0000036012625,0.00007994419],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908525,0.000036185276,0.00011720893,0.00037121712,0.00017987784,0.00021026122],"domain_scores_gemma":[0.99838156,0.0009624026,0.000021744774,0.00046977092,0.0000826264,0.00008190625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032174087,0.00006758565,0.00009112243,0.000026008916,0.00011143561,0.00009752661,0.0011474814,0.000031431748,0.000010616025],"category_scores_gemma":[0.0008991226,0.000053264688,0.00002045956,0.00012347757,0.00003240696,0.00019825065,0.00055169343,0.000059328682,0.000057114386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006645517,0.000055435303,0.00008802906,0.000015167407,0.0000061595533,0.0000018201624,0.000043916327,0.0010210206,0.00007105569,0.58022684,0.0046266103,0.41383728],"study_design_scores_gemma":[0.00013436055,0.0000771181,0.00074780686,0.000004359668,0.0000017779496,0.0000033361555,0.000002180874,0.7988533,0.000043273674,0.19040075,0.009655913,0.00007584426],"about_ca_topic_score_codex":0.0000025720037,"about_ca_topic_score_gemma":0.0000012151148,"teacher_disagreement_score":0.79783225,"about_ca_system_score_codex":0.000010595776,"about_ca_system_score_gemma":0.0000421554,"threshold_uncertainty_score":0.21720709},"labels":[],"label_agreement":null},{"id":"W2588897368","doi":"10.21825/ichmet.71281","title":"Relative Risk Approach: a Methodology for the Ecological Risk Classification of Inorganic Substances","year":2016,"lang":"en","type":"article","venue":"Proceedings of the 18th International Conference on Heavy Metals in the Environment","topic":"Statistical and Computational Modeling","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":"Environment and Climate Change Canada","funders":"","keywords":"Computer science; Risk management; Risk analysis (engineering); Ecology; Business; Biology","score_opus":0.1664301107248998,"score_gpt":0.3213469327619957,"score_spread":0.15491682203709592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588897368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16497554,0.00007374681,0.82736295,0.0059405062,0.00016793016,0.0004999573,0.00004133498,0.000008259909,0.00092978147],"genre_scores_gemma":[0.9435514,0.00023190776,0.05587947,0.00008702995,0.000029369148,0.00016705946,7.0460635e-7,0.0000041926396,0.00004881985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984368,0.00017863917,0.00043181403,0.0003133559,0.00049766555,0.00014168801],"domain_scores_gemma":[0.9958947,0.003173207,0.0006148254,0.00017783942,0.000119672535,0.00001979495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021725544,0.00012304532,0.00018635407,0.00005365213,0.00011954316,0.000028676233,0.0016989968,0.00005229391,0.00002441658],"category_scores_gemma":[0.0012096018,0.00004985635,0.00010595642,0.0001191042,0.00028593696,0.00016764883,0.00019806525,0.00017980371,0.000004180419],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007200756,0.00015050267,0.0016488812,0.0000059794747,0.00006955696,3.4041637e-8,0.0007138509,0.0006490551,0.0024116735,0.9838599,0.000018767401,0.0103998],"study_design_scores_gemma":[0.00038339887,0.00019264768,0.08519722,0.00003566312,0.000046815396,0.0000025766471,0.00043881196,0.19335362,0.004793733,0.7153741,0.00008722292,0.00009421088],"about_ca_topic_score_codex":0.000010914921,"about_ca_topic_score_gemma":0.000001363144,"teacher_disagreement_score":0.7785759,"about_ca_system_score_codex":0.000069270376,"about_ca_system_score_gemma":0.000023835477,"threshold_uncertainty_score":0.3157187},"labels":[],"label_agreement":null},{"id":"W2594829468","doi":"10.5539/jmr.v9n2p32","title":"On Finding Geodesic Equation of Student T Distribution","year":2017,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Statistical and Computational Modeling","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; Geodesic; Distribution (mathematics); Focus (optics); Partial differential equation; Differential equation; Mathematical analysis","score_opus":0.3772508017875866,"score_gpt":0.49836056064224904,"score_spread":0.12110975885466246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594829468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20011596,0.000014341914,0.7988467,0.0005315902,0.00008716637,0.000049349124,0.0000020440928,0.000002348541,0.0003504551],"genre_scores_gemma":[0.9429818,0.000009028532,0.056919336,0.000004773271,0.000046386187,0.0000012285833,5.3484024e-7,0.000002815695,0.000034092966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979501,0.000074228294,0.00038973725,0.00007931694,0.0013618714,0.00014475525],"domain_scores_gemma":[0.9972775,0.0012042691,0.00040534124,0.00027911045,0.00077029306,0.0000635066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025658235,0.000048481903,0.0001442048,0.000120059594,0.00025659843,0.00022545342,0.0009637074,0.000026037058,0.0000075453563],"category_scores_gemma":[0.0014321189,0.00003644407,0.000052898216,0.00008114159,0.00005812481,0.00024124116,0.00019227699,0.0002291019,0.000014743978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009281647,0.00029619914,0.0001173939,0.00005580559,0.00002200952,0.000015713902,0.0005110303,0.0041450225,0.0003381346,0.9819539,0.00030375418,0.012231717],"study_design_scores_gemma":[0.00022784232,0.00025144403,0.004714673,0.00020573725,0.0000034845773,0.000011656073,0.00006389615,0.43838072,0.00076268404,0.5553312,0.000008955069,0.000037732687],"about_ca_topic_score_codex":0.0000026652365,"about_ca_topic_score_gemma":2.8092552e-7,"teacher_disagreement_score":0.74286586,"about_ca_system_score_codex":0.000074052055,"about_ca_system_score_gemma":0.00009609499,"threshold_uncertainty_score":0.21740524},"labels":[],"label_agreement":null},{"id":"W2617645851","doi":"10.1007/978-3-319-59876-5_45","title":"Classification Boosting by Data Decomposition Using Consensus-Based Combination of Classifiers","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Statistical and Computational Modeling","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; Boosting (machine learning); Classifier (UML); Decomposition; Artificial intelligence; Generalization; Machine learning; Data mining; Pattern recognition (psychology); Mathematics","score_opus":0.09257301002347595,"score_gpt":0.3379655773033772,"score_spread":0.24539256727990122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617645851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031348437,0.00015459277,0.99700475,0.00071328477,0.0007352687,0.0002724002,0.00006675699,0.00006626957,0.00067318644],"genre_scores_gemma":[0.48427564,0.0000038602857,0.515264,0.00023981625,0.00007356072,0.0000018050089,0.00010826855,0.000015651134,0.000017422013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664533,0.000053636864,0.0006610496,0.0013005775,0.0009832558,0.00035615094],"domain_scores_gemma":[0.99582267,0.0010367325,0.00082243147,0.0016877137,0.0005133342,0.000117138334],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089606736,0.00033639156,0.00041418575,0.00045491624,0.00043950163,0.0004228793,0.0033508479,0.00022332105,0.00000466882],"category_scores_gemma":[0.00028548404,0.00034517603,0.00006115165,0.00018685247,0.0007453277,0.00051410863,0.0008723644,0.00042377203,0.000004981805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023096381,0.00011052806,0.00015114939,0.0001258749,0.000023437084,0.000026101721,0.00014390599,0.18039444,0.004095431,0.08447992,0.00007756439,0.7303485],"study_design_scores_gemma":[0.00027003154,0.00006671655,0.00015415218,0.00039301455,0.000013104109,0.000013409114,1.2396853e-7,0.8691189,0.000602405,0.12900963,0.000051161474,0.00030730866],"about_ca_topic_score_codex":0.00004559787,"about_ca_topic_score_gemma":0.0000121142775,"teacher_disagreement_score":0.7300412,"about_ca_system_score_codex":0.00025002446,"about_ca_system_score_gemma":0.0007857773,"threshold_uncertainty_score":0.99990004},"labels":[],"label_agreement":null},{"id":"W2724538333","doi":"","title":"Quasi-Topological Structure of Extensions in Logic of Determination of Objects (LDO) for Typical and Atypical objects.","year":2017,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Statistical and Computational Modeling","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":"Topology (electrical circuits); Computer science; Mathematics; Algebra over a field; Artificial intelligence; Pure mathematics; Combinatorics","score_opus":0.11418553975170798,"score_gpt":0.4244331211620217,"score_spread":0.3102475814103137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724538333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5455208,0.00007315467,0.45256284,0.0014633749,0.00007491149,0.00025601438,0.000019278245,0.000006325397,0.00002331228],"genre_scores_gemma":[0.94768596,0.000022431866,0.05217165,0.000051476218,0.000047014626,0.000008914831,0.0000017133481,0.000003152547,0.000007713428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986719,0.00014841092,0.0002654715,0.00023353352,0.00044958765,0.00023112292],"domain_scores_gemma":[0.99722445,0.0017638291,0.00010185233,0.00036064806,0.00049605523,0.000053170395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000827322,0.00007423436,0.00022058963,0.000037507118,0.00025427414,0.00003647033,0.0006846207,0.00009767329,0.000004746836],"category_scores_gemma":[0.0013808474,0.00004736824,0.00008480013,0.00014981908,0.0007316779,0.00011826829,0.00047374572,0.00028429204,3.369957e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005358023,0.0002554007,0.0019102909,0.00024189278,0.0000265994,0.000004104935,0.003693026,0.00034944882,0.0114153605,0.9607508,0.0001735044,0.020643761],"study_design_scores_gemma":[0.0003610667,0.00034196294,0.08446675,0.00003858444,0.00000381993,0.000003559976,0.00017870183,0.4226603,0.0019952508,0.48989707,0.0000028240866,0.000050138213],"about_ca_topic_score_codex":0.00003590317,"about_ca_topic_score_gemma":0.000018389195,"teacher_disagreement_score":0.47085375,"about_ca_system_score_codex":0.000021382333,"about_ca_system_score_gemma":0.00011361618,"threshold_uncertainty_score":0.26958978},"labels":[],"label_agreement":null},{"id":"W2777844264","doi":"","title":"Joint simulation of a random function and its derivatives.","year":2000,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Statistical and Computational Modeling","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":"Centre de Géomatique du Québec","funders":"","keywords":"Joint (building); Computer science; Function (biology); Random function; Applied mathematics; Random variable; Algorithm; Mathematical optimization; Mathematics; Statistics; Engineering; Structural engineering","score_opus":0.02618228545819924,"score_gpt":0.2407215085165463,"score_spread":0.21453922305834708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777844264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047460705,0.0008357856,0.9451516,0.0018331199,0.00010566859,0.0002911337,0.0000205026,0.00012372769,0.004177753],"genre_scores_gemma":[0.91118294,0.0001381162,0.08811536,0.00006832957,0.000013715373,0.000026781196,0.00007659353,0.000013634407,0.00036455604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964647,0.001800373,0.00053440954,0.00061858713,0.0003963729,0.00018550082],"domain_scores_gemma":[0.9954804,0.0018312266,0.000373054,0.0007158352,0.0014872414,0.000112249574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017482528,0.00022096962,0.00033281642,0.0001691211,0.0001764788,0.00022081251,0.0005826304,0.00014853523,0.00004893767],"category_scores_gemma":[0.0007576765,0.00022726058,0.000107449116,0.00026944748,0.00008703595,0.00021000674,0.0008166579,0.00030399,0.000010814942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005903252,0.00039176265,0.00013544547,0.0002838198,0.00010669864,0.000002353966,0.005672232,0.13795547,0.0013021281,0.65683484,0.00005861615,0.1971976],"study_design_scores_gemma":[0.0005510639,5.771435e-7,0.0029535722,0.0005431092,0.000019215184,0.000002010535,0.000008149318,0.87302357,0.002408993,0.120157786,0.00014404321,0.00018788432],"about_ca_topic_score_codex":0.00010331629,"about_ca_topic_score_gemma":0.000019606263,"teacher_disagreement_score":0.8637222,"about_ca_system_score_codex":0.000036028097,"about_ca_system_score_gemma":0.00015611926,"threshold_uncertainty_score":0.9267417},"labels":[],"label_agreement":null},{"id":"W2894056097","doi":"","title":"VALIDATION OF EXPERIMENTAL METHODOLOGY FOR STATE MINDFULNESS INDUCTION IN A CONTROLLED LABORATORY SETTING","year":2018,"lang":"en","type":"article","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"Statistical and Computational Modeling","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":"Mindfulness; Psychology; Computer science; Psychotherapist","score_opus":0.022742050869797973,"score_gpt":0.2617013522581649,"score_spread":0.2389593013883669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894056097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045052223,0.000057569854,0.9486829,0.00029075894,0.00046961015,0.00038088526,0.000027831387,0.000049379873,0.004988863],"genre_scores_gemma":[0.8861841,0.000005073882,0.1125194,0.00091339287,0.00018763449,0.000077296805,0.00009779307,0.000005112017,0.000010183628],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987835,0.00018533827,0.00056508486,0.00015064463,0.000112181115,0.00020323737],"domain_scores_gemma":[0.99913496,0.00037907987,0.00024919063,0.000103101636,0.00008081387,0.000052843257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040498484,0.00011082024,0.000220267,0.00014264052,0.00009964519,0.000096589756,0.00016062615,0.00006262538,0.000018880142],"category_scores_gemma":[0.000038286616,0.00010167346,0.000035815265,0.00028740085,0.000051122042,0.005497637,0.00010251827,0.000080410515,0.0000025248198],"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":[0.00048609328,0.000043565058,0.00009943124,0.000036401423,0.00002044283,3.0755777e-7,0.0010304723,0.0034253397,0.0001193258,0.9605579,0.00030283985,0.033877913],"study_design_scores_gemma":[0.005805464,0.00037458228,0.0010233289,0.00012667451,0.0000079047195,0.000006319457,0.000049688995,0.88429576,0.049400404,0.053620912,0.005007317,0.00028165017],"about_ca_topic_score_codex":9.180159e-8,"about_ca_topic_score_gemma":1.0134647e-7,"teacher_disagreement_score":0.90693694,"about_ca_system_score_codex":0.000008440407,"about_ca_system_score_gemma":0.00005230782,"threshold_uncertainty_score":0.41461232},"labels":[],"label_agreement":null},{"id":"W3096676893","doi":"10.1016/j.cie.2020.106947","title":"Developing machine-learning regression model with Logical Analysis of Data (LAD)","year":2020,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Statistical and Computational Modeling","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial regression; Regression analysis; Regression diagnostic; Decision tree; Random forest; Mathematics; Mean squared error; Linear regression; Support vector machine; Regression; Binary classification; Statistics; Proper linear model; Data set; Artificial intelligence; Data mining; Computer science","score_opus":0.1801358890617506,"score_gpt":0.28639402361284566,"score_spread":0.10625813455109506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096676893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008280337,0.000049906557,0.99058753,0.00070021336,0.00009219827,0.00007429549,0.000012812107,0.00018800824,0.000014695859],"genre_scores_gemma":[0.54873663,0.0000020209775,0.4509889,0.00013445257,0.00007837294,0.000001498202,0.0000490703,0.00000789479,0.0000011531895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984745,0.00003647857,0.0003584777,0.0005253172,0.0003752854,0.00022998039],"domain_scores_gemma":[0.9990314,0.0003142209,0.00011514208,0.000315107,0.000077818266,0.00014629128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016606759,0.00018770182,0.00039126188,0.00018092309,0.000067377914,0.00008810372,0.0011662258,0.00008077902,0.000002420526],"category_scores_gemma":[0.00013930541,0.00015106905,0.00005969307,0.0014266965,0.000019167426,0.00036230969,0.0008297436,0.00036005894,0.0000015663223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015624764,0.000008269945,0.00018494949,0.000009856464,0.0001902157,0.000013753099,0.00013750192,0.9616393,0.000078456666,0.020799622,0.000022238191,0.016900204],"study_design_scores_gemma":[0.00037393608,0.00006195829,0.00010706851,0.000069064125,0.00009119332,0.000002706187,0.000004484679,0.99874884,0.00007508113,0.00011399931,0.00014909811,0.00020255521],"about_ca_topic_score_codex":0.000011836709,"about_ca_topic_score_gemma":4.7229378e-7,"teacher_disagreement_score":0.5404563,"about_ca_system_score_codex":0.00003759444,"about_ca_system_score_gemma":0.00011626175,"threshold_uncertainty_score":0.6160417},"labels":[],"label_agreement":null},{"id":"W3170257233","doi":"10.6000/1929-6029.2021.10.06","title":"Existing Approaches and Development Perspectives for Inferences","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical and Computational Modeling","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":"Dilemma; Simple (philosophy); Computer science; Management science; Resolution (logic); Data science; Statistical inference; Development (topology); Mathematics; Artificial intelligence; Statistics; Epistemology; Engineering","score_opus":0.33896670534320916,"score_gpt":0.48128146677752254,"score_spread":0.14231476143431337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170257233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004268375,0.00048593318,0.9916386,0.0029545974,0.00022688456,0.000036999605,0.000008449946,0.000002999564,0.0003771312],"genre_scores_gemma":[0.40579233,0.00012437365,0.59386003,0.000053381893,0.00013281869,0.0000051688844,0.0000034130856,0.0000026126727,0.000025848705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971772,0.00013439034,0.0004310135,0.00017544907,0.001904446,0.00017752184],"domain_scores_gemma":[0.9930221,0.0044920375,0.00007930528,0.00004956536,0.0022074317,0.0001495897],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002261928,0.00005661594,0.00012910872,0.00022036025,0.00007071777,0.0001760861,0.0005925063,0.00003983975,0.00003881572],"category_scores_gemma":[0.010676256,0.00004824895,0.000015708603,0.00014759769,0.00013291316,0.0001314987,0.00027865052,0.00036306685,0.0000016514789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015956042,0.00008357887,0.00034551957,0.0000132363475,0.000022685723,0.0002777561,0.001388936,0.00010840904,0.0000059217555,0.6161397,0.00013320126,0.3814651],"study_design_scores_gemma":[0.0009634064,0.00011396199,0.0057498175,0.00028163835,0.0000018804973,0.00027682562,0.00242795,0.40437675,0.00015422847,0.58248234,0.003053002,0.00011822829],"about_ca_topic_score_codex":0.000006528142,"about_ca_topic_score_gemma":0.000020238629,"teacher_disagreement_score":0.40426832,"about_ca_system_score_codex":0.00009697629,"about_ca_system_score_gemma":0.0013516486,"threshold_uncertainty_score":0.99765724},"labels":[],"label_agreement":null},{"id":"W3172827680","doi":"10.48550/arxiv.2103.08148","title":"On statistical estimation and inferences in optional regression models","year":2021,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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":"Consistency (knowledge bases); Statistical inference; Mathematics; Semimartingale; Regression; Least-squares function approximation; Property (philosophy); Regression analysis; Estimation; Statistics; Robust regression; Econometrics; Applied mathematics; Computer science; Estimator; Engineering","score_opus":0.06361129981521921,"score_gpt":0.35757615149783833,"score_spread":0.2939648516826191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172827680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28722036,0.00011340254,0.7036673,0.0007918103,0.0002665044,0.00035778113,0.00004359589,0.000036229405,0.0075030527],"genre_scores_gemma":[0.8352618,0.0006464562,0.16382095,0.00005823289,0.000025876707,0.00007479008,0.00007417979,0.000008729901,0.000028972738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772453,0.0002588142,0.00045108725,0.00083976524,0.0003846615,0.00034115103],"domain_scores_gemma":[0.99733275,0.0020193425,0.00007421712,0.00033655562,0.00011112245,0.00012602206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079472386,0.00017887076,0.00029320034,0.00036777445,0.00008672862,0.0003682249,0.00043872066,0.00018704169,0.00001747438],"category_scores_gemma":[0.00052195386,0.00017458311,0.000030690375,0.00011863718,0.00012511261,0.000229821,0.0010799206,0.0009624469,0.000002513637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000139895155,0.000053982985,0.00010774163,0.00003222397,0.0000043248233,0.000026288611,0.00013475158,0.50165033,0.000001327379,0.22003925,0.00000286385,0.27793294],"study_design_scores_gemma":[0.00013615793,0.000029741057,0.0035758947,0.00023608917,5.4279536e-7,0.000003429327,0.000029697907,0.6177165,0.000002315114,0.37816006,0.0000028046568,0.000106784435],"about_ca_topic_score_codex":0.00006025795,"about_ca_topic_score_gemma":0.00006342048,"teacher_disagreement_score":0.54804146,"about_ca_system_score_codex":0.00029195726,"about_ca_system_score_gemma":0.0006189655,"threshold_uncertainty_score":0.71192926},"labels":[],"label_agreement":null},{"id":"W3173624615","doi":"10.20944/preprints202106.0474.v1","title":"GMDH Neural Networks - Based Modeling of Variable Power Inductor","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Statistical and Computational Modeling","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":"Inductor; Inductance; Artificial neural network; Control theory (sociology); Power (physics); Nonlinear system; Voltage; Computer science; Variable (mathematics); Electronic engineering; Topology (electrical circuits); Engineering; Mathematics; Artificial intelligence; Electrical engineering; Physics","score_opus":0.12709957493673452,"score_gpt":0.3223365937671903,"score_spread":0.1952370188304558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173624615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2399815,0.00012357045,0.75744826,0.00018053492,0.0010109397,0.00018485574,0.00000857449,0.00012821009,0.00093354913],"genre_scores_gemma":[0.882892,0.000005308235,0.11650963,0.0003490153,0.0001044249,0.000041417497,0.000041823412,0.00002402652,0.00003234224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99681026,0.00019546915,0.00075914233,0.0012237437,0.0005986755,0.0004127083],"domain_scores_gemma":[0.99730694,0.00027221107,0.00027695988,0.0013593832,0.00060741516,0.0001770981],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054622965,0.00034732308,0.0005341544,0.00013345187,0.00009490572,0.00010060253,0.0015647694,0.00029873056,0.0003061401],"category_scores_gemma":[0.0002723551,0.00036655462,0.00021018038,0.0003444889,0.000048934082,0.00021377666,0.00344736,0.00095112424,0.000026685164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011476318,0.00011411406,0.004413537,0.000073982366,0.00005335441,0.000015062071,0.00017807687,0.98091924,0.00021302236,0.0135631,0.0000039982224,0.00044105158],"study_design_scores_gemma":[0.00019355243,0.000012323008,0.0033169915,0.00017944243,0.000022784601,0.000004539628,0.000014788206,0.9781261,0.00034479672,0.017435458,0.0000074037894,0.00034178954],"about_ca_topic_score_codex":0.00023656143,"about_ca_topic_score_gemma":0.0000015859483,"teacher_disagreement_score":0.64291054,"about_ca_system_score_codex":0.00007583065,"about_ca_system_score_gemma":0.00044671094,"threshold_uncertainty_score":0.99987864},"labels":[],"label_agreement":null},{"id":"W3212767849","doi":"10.5539/mas.v15n6p46","title":"New Systematic Solution for Resolving Nonlinear Dynamics Using System Analytical Theory Based on Engineering Science","year":2021,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Statistical and Computational Modeling","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; Field (mathematics); Physicist; Imperfect; Nonlinear system; Systems science; Management science; Science and engineering; Physics; Theoretical physics; Mathematics; Engineering ethics; Artificial intelligence; Engineering; Quantum mechanics","score_opus":0.022623590672645827,"score_gpt":0.2581443445565378,"score_spread":0.23552075388389196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212767849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016148161,0.000020636442,0.99702007,0.0000918894,0.00031835944,0.00033196493,0.00000441012,0.0001678677,0.00043000822],"genre_scores_gemma":[0.5454345,5.7516083e-8,0.45441997,0.0000769306,0.000037360063,0.000012326578,0.0000011744966,0.000006988938,0.000010738693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968075,0.000027259104,0.00039251635,0.0009175401,0.0012514441,0.0006037186],"domain_scores_gemma":[0.9979658,0.00065461866,0.0001040056,0.00058857177,0.00040355916,0.00028344637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00203191,0.00018541756,0.00027425593,0.00032434324,0.000739779,0.000673428,0.0012010937,0.000042606112,9.4755956e-7],"category_scores_gemma":[0.00056637515,0.00017221007,0.00006308259,0.0018246277,0.00019564539,0.0003738281,0.000302227,0.00013257522,0.0000072508865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059821955,0.000015997759,0.000001221686,0.00044678248,0.000002261685,0.000004112299,0.00007477011,0.36638838,0.010986913,0.6202697,5.9530794e-7,0.0018032693],"study_design_scores_gemma":[0.00019586463,0.000020501013,0.000018203717,0.0006367376,0.000016175138,0.000016521128,0.00003831516,0.9853638,0.0015513952,0.011937187,3.4751e-7,0.0002049505],"about_ca_topic_score_codex":0.0000040771524,"about_ca_topic_score_gemma":7.6736706e-7,"teacher_disagreement_score":0.6189754,"about_ca_system_score_codex":0.0007606423,"about_ca_system_score_gemma":0.0017536146,"threshold_uncertainty_score":0.70225227},"labels":[],"label_agreement":null},{"id":"W4200244830","doi":"10.1017/9781108938051.010","title":"Learning Discriminative Models in General","year":2021,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"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":"Discriminative model; Computer science; Content (measure theory); Information retrieval; Artificial intelligence; Mathematics","score_opus":0.03986609024237241,"score_gpt":0.21844699696100972,"score_spread":0.1785809067186373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200244830","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000048409147,0.00005409544,0.4481412,0.000016597207,0.00007378695,0.00007048673,0.000015644986,0.000050326555,0.55152947],"genre_scores_gemma":[0.005795649,0.000033751738,0.014109701,0.00006529266,0.000056029305,6.999809e-7,0.000036653353,0.000017860752,0.9798844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986448,0.00006024978,0.00017442551,0.00059102674,0.00029794767,0.00023152838],"domain_scores_gemma":[0.9992142,0.00013392304,0.00010381445,0.0002554394,0.00018432118,0.00010832045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000666079,0.00024239367,0.00029796356,0.0001630934,0.00012033784,0.00008529979,0.00060464407,0.00015230004,0.0000015510402],"category_scores_gemma":[0.000009891341,0.000290619,0.00011552567,0.00001573673,0.00007395928,0.00019801392,0.00075321307,0.0005303671,0.0000044449525],"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":[0.000007995423,0.0000068341146,3.258877e-7,0.000020359028,0.000025722038,0.0006007767,0.00013827463,0.010896927,0.000003865187,0.9808321,0.00034378635,0.0071230102],"study_design_scores_gemma":[0.00044483837,0.00005145935,0.000017297958,0.00022236562,0.000036004905,0.000018432725,0.000041590378,0.94652724,0.00003290463,0.0062489687,0.04580349,0.00055543217],"about_ca_topic_score_codex":0.00010890401,"about_ca_topic_score_gemma":0.0000020626242,"teacher_disagreement_score":0.97458315,"about_ca_system_score_codex":0.00016847873,"about_ca_system_score_gemma":0.00014466807,"threshold_uncertainty_score":0.9999546},"labels":[],"label_agreement":null},{"id":"W4200418944","doi":"10.1155/2021/9780860","title":"Environmental Systems Modelling and Analysis under Changing Conditions","year":2021,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Statistical and Computational Modeling","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 Prince Edward Island","funders":"","keywords":"Computer science; Environmental science; Biochemical engineering; Risk analysis (engineering); Engineering; Business","score_opus":0.018291802483267578,"score_gpt":0.21365064310569076,"score_spread":0.19535884062242317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200418944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01331724,0.0002753167,0.9860747,0.00007326056,0.000037650974,0.00006268941,0.000004211061,0.000058244634,0.00009667852],"genre_scores_gemma":[0.8957488,0.0000075402168,0.104154825,0.000019748446,0.000011233177,0.00002310221,0.000006472228,0.0000063431853,0.00002194767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991367,0.000014979053,0.000238045,0.0002339445,0.00016713211,0.0002091989],"domain_scores_gemma":[0.9995334,0.00023642989,0.000020347552,0.00013315004,0.000011112582,0.00006551635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014394475,0.00009442951,0.00017761398,0.0001877592,0.000045788776,0.000107810316,0.00009923451,0.000032910597,0.000010460362],"category_scores_gemma":[0.000015588195,0.00009499283,0.000036829384,0.00047250406,0.000012307183,0.00013704118,0.00012187815,0.000099035424,0.0000071888453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.0414574e-8,0.00001836447,0.00002457668,0.000050256647,0.00002793132,0.0000065758877,0.00015961648,0.6922632,0.00008900838,0.3073013,2.3077432e-7,0.00005888057],"study_design_scores_gemma":[0.00007113371,0.0000032788278,0.00007623706,0.00006430645,0.00002149658,0.000020754158,0.000053918542,0.9231145,0.000017717968,0.07644984,0.0000027856022,0.00010406686],"about_ca_topic_score_codex":0.000001568115,"about_ca_topic_score_gemma":1.510409e-7,"teacher_disagreement_score":0.88243157,"about_ca_system_score_codex":0.000036405014,"about_ca_system_score_gemma":0.000007751979,"threshold_uncertainty_score":0.3873695},"labels":[],"label_agreement":null},{"id":"W4229930697","doi":"10.4018/978-1-59904-951-9.ch112","title":"A TOPSIS Data Mining Demonstration and Application to Credit Scoring","year":2008,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Statistical and Computational Modeling","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":"TOPSIS; Ideal solution; Computer science; Data mining; Similarity (geometry); Classifier (UML); Machine learning; Artificial intelligence; Mathematics; Operations research","score_opus":0.04839037927444889,"score_gpt":0.27725182929370307,"score_spread":0.22886145001925418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229930697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007170796,0.00011428265,0.751579,0.0001261454,0.0001287522,0.00018054996,0.00010330547,0.00009223101,0.24760403],"genre_scores_gemma":[0.30455017,0.000013799413,0.689042,0.0012398128,0.0007592852,0.000043244112,0.000067696026,0.00003341227,0.004250587],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984179,0.000008318886,0.00029278168,0.0007414044,0.0003593333,0.00018022502],"domain_scores_gemma":[0.99893177,0.000083356004,0.000102504884,0.00061729667,0.00010202919,0.00016306856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081859995,0.0002105649,0.0002041829,0.000053869157,0.00015224233,0.00015143212,0.00071999704,0.00011709843,0.0000014349993],"category_scores_gemma":[0.000028568029,0.00022604501,0.000028100734,0.000029151446,0.000037900936,0.00014069316,0.00068455917,0.00010246052,0.00003096484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003887596,0.0000023186599,0.0000072466864,0.000008438195,0.000012976885,0.000012247222,0.00004584655,0.00015809297,0.000004941478,0.78744954,0.00069112144,0.21160333],"study_design_scores_gemma":[0.00021442365,0.00009193898,0.00025268865,0.00021559896,0.000040645285,0.00019711883,0.000005942353,0.4514602,0.000009320159,0.538011,0.008893346,0.0006077495],"about_ca_topic_score_codex":0.00003872703,"about_ca_topic_score_gemma":0.000023229504,"teacher_disagreement_score":0.45130214,"about_ca_system_score_codex":0.000068111454,"about_ca_system_score_gemma":0.00013520992,"threshold_uncertainty_score":0.9217848},"labels":[],"label_agreement":null},{"id":"W4233169351","doi":"10.1515/iupac.81.0790","title":"R-Strategy","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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; Ecotoxicology; Relation (database); Environmental risk assessment; Computer science; Ecology; Risk assessment; Biology; Data mining; Linguistics; Philosophy","score_opus":0.023849473111591874,"score_gpt":0.40354046248521874,"score_spread":0.3796909893736269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233169351","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001038895,0.00016342997,0.4317177,0.0004888518,0.0003896872,0.000060981976,0.5670711,0.00006791186,0.00003935779],"genre_scores_gemma":[0.00002734568,0.000117011106,0.00457735,0.00053998426,0.00060491497,0.000009699528,0.99394923,0.000013254647,0.00016118238],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973706,0.000064202235,0.00040872846,0.0006177238,0.0011540275,0.00038474234],"domain_scores_gemma":[0.99817395,0.00024813905,0.0001514794,0.00072248676,0.0005159128,0.00018802939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659608,0.00031844096,0.0003748906,0.00014223135,0.000109971596,0.00019075342,0.0012593427,0.00018848028,0.0005524756],"category_scores_gemma":[0.00022176796,0.00023096558,0.00011137711,0.00020706341,0.00006999286,0.00017890055,0.00037233363,0.00030654934,0.0000110457295],"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":[0.000010217676,0.00006633728,1.9894732e-7,0.000029448292,0.000027468486,0.00007363201,0.0000024367607,0.00007254764,6.55759e-7,0.006146441,0.9542977,0.039272904],"study_design_scores_gemma":[0.00030315557,0.00012731808,0.000008737808,0.0001293447,0.00001565042,0.000022159744,0.0000012472932,0.004781342,0.0000019809208,0.039434206,0.9548458,0.00032906016],"about_ca_topic_score_codex":0.000043497934,"about_ca_topic_score_gemma":0.000040445222,"teacher_disagreement_score":0.42714033,"about_ca_system_score_codex":0.00016137728,"about_ca_system_score_gemma":0.0008591014,"threshold_uncertainty_score":0.9418503},"labels":[],"label_agreement":null},{"id":"W4233520248","doi":"10.1515/iupac.76.0140","title":"Bioconcentration","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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; Toxicokinetics; Bioconcentration; Relation (database); Toxicology; Computer science; Medicine; Environmental chemistry; Chemistry; Pharmacology; Biology; Data mining; Philosophy; Linguistics; Bioaccumulation","score_opus":0.017673142858699215,"score_gpt":0.3918131703678291,"score_spread":0.3741400275091299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233520248","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.280819e-7,0.000115148374,0.4627952,0.0007442684,0.00073771254,0.00007342985,0.53546816,0.00005428892,0.000010883974],"genre_scores_gemma":[0.000023340617,0.00011780051,0.006047002,0.00060367346,0.0011840666,0.000009896619,0.99195015,0.000009204323,0.000054862907],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977747,0.00005826243,0.0003612253,0.0005155221,0.0009887164,0.00030155998],"domain_scores_gemma":[0.99851024,0.00017825922,0.00015214908,0.00054319034,0.00047701775,0.00013912107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022349803,0.00024986733,0.00027579488,0.00010977699,0.00010366909,0.00016877711,0.0008405302,0.0001567655,0.00032083766],"category_scores_gemma":[0.00022504863,0.00018451581,0.00008377299,0.00018259132,0.00005625995,0.00020081805,0.0002322905,0.00020272304,0.000006826694],"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":[0.000008813121,0.000055152337,3.51158e-7,0.000022815373,0.000019422467,0.000018942816,0.000004135117,0.000025646545,0.0000016433451,0.005224572,0.97225565,0.022362873],"study_design_scores_gemma":[0.0002936012,0.0000803671,0.0000070154065,0.00012427122,0.000013478718,0.000008135097,9.864585e-7,0.0062652742,0.0000057709594,0.021489229,0.9714485,0.000263391],"about_ca_topic_score_codex":0.000027175633,"about_ca_topic_score_gemma":0.000027231112,"teacher_disagreement_score":0.4567482,"about_ca_system_score_codex":0.00018468128,"about_ca_system_score_gemma":0.0006411472,"threshold_uncertainty_score":0.75243366},"labels":[],"label_agreement":null},{"id":"W4236196134","doi":"10.1002/9781118787106.app2","title":"Appendix B: An Inventory of Discrete Distributions","year":2013,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Class (philosophy); Hierarchy; Mathematics; Combinatorics; Computer science; Law; Artificial intelligence; Political science","score_opus":0.01957056135727222,"score_gpt":0.2575053481440117,"score_spread":0.2379347867867395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236196134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006665297,0.00036481954,0.9912716,0.000070595626,0.00018381148,0.00030593618,0.001900493,0.00006990935,0.0057661724],"genre_scores_gemma":[0.00281602,0.000257921,0.98646265,0.000035836263,0.00005481258,0.00006529916,0.00071099075,0.00005600887,0.009540483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986529,0.000118639975,0.00038467644,0.00041694377,0.00022243655,0.00020440415],"domain_scores_gemma":[0.99913913,0.00013284457,0.00015288289,0.00039263218,0.00007245342,0.00011005734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015140281,0.00019204052,0.00032039997,0.000084561456,0.00004870044,0.000064314074,0.0003697999,0.00012753018,0.00022839567],"category_scores_gemma":[0.00015871834,0.0001802949,0.000021975771,0.00014458828,0.0003848896,0.00019515686,0.00021809139,0.00017083785,0.000016498147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050854896,0.000074548,0.0003934657,0.00026075527,0.000011446184,0.0000038776407,0.00014994589,0.000071214214,7.351139e-7,0.97606534,0.010949198,0.012014379],"study_design_scores_gemma":[0.00014786751,0.00015019784,0.0005949943,0.0002225434,0.000010579645,0.0000053526205,0.000019844172,0.10145842,0.0000021491992,0.8867768,0.010353802,0.0002574514],"about_ca_topic_score_codex":0.00037886918,"about_ca_topic_score_gemma":0.00035610568,"teacher_disagreement_score":0.10138721,"about_ca_system_score_codex":0.000031756823,"about_ca_system_score_gemma":0.00009856196,"threshold_uncertainty_score":0.73522127},"labels":[],"label_agreement":null},{"id":"W4236282401","doi":"10.1002/9781118787106.app3","title":"Appendix C: Discretization of the Severity Distribution","year":2013,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Rounding; Discretization; Mathematics; Distribution (mathematics); Statistics; Applied mathematics; Computer science; Mathematical analysis","score_opus":0.01035412829163823,"score_gpt":0.22176803027619077,"score_spread":0.21141390198455254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236282401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056317134,0.00015577872,0.9939769,0.00019142198,0.00024411686,0.00037071676,0.0014378602,0.000037012323,0.0035298923],"genre_scores_gemma":[0.019294467,0.0004838688,0.950731,0.000119113894,0.00010217063,0.00010499958,0.0011333326,0.00009204605,0.02793904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99891573,0.00011831429,0.0003005123,0.00029295406,0.00023372698,0.00013876522],"domain_scores_gemma":[0.99927497,0.00013273164,0.00016831094,0.00031746496,0.00006926129,0.000037245114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012979744,0.00014615552,0.00021569093,0.000025792351,0.00005422604,0.00005155957,0.00032333718,0.00011093856,0.000070417336],"category_scores_gemma":[0.000247458,0.00010875224,0.000021468191,0.0001791388,0.00029452294,0.000105173356,0.00024720415,0.00014547455,0.000006981105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003817566,0.000036618105,0.0007863591,0.00023287417,0.0000064043893,5.671771e-7,0.000087086584,0.00012901565,3.572399e-7,0.96566045,0.013190862,0.019865606],"study_design_scores_gemma":[0.00013354259,0.000042762604,0.00543994,0.00031259435,0.000010415912,0.0000062855024,0.000010338069,0.13256034,0.000006137341,0.8496777,0.011595571,0.00020437903],"about_ca_topic_score_codex":0.00025562468,"about_ca_topic_score_gemma":0.00020225116,"teacher_disagreement_score":0.13243133,"about_ca_system_score_codex":0.00003441898,"about_ca_system_score_gemma":0.000071002505,"threshold_uncertainty_score":0.44347876},"labels":[],"label_agreement":null},{"id":"W4238598851","doi":"10.1007/978-3-540-72816-0_12616","title":"labrador hornblende","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical and Computational Modeling","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":"Hornblende; Geology; Paleontology","score_opus":0.02453545776856449,"score_gpt":0.23480109903725682,"score_spread":0.21026564126869232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238598851","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.0639282e-7,0.000083875144,0.51242775,0.0005070878,0.00008455643,0.000032843178,0.0000027197043,0.000116156,0.4867449],"genre_scores_gemma":[0.00043851061,0.000019729847,0.2551203,0.0017430529,0.00014006715,0.000001303486,0.000008543214,0.00001047327,0.742518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99896365,0.000004160806,0.00019622046,0.0003732761,0.00031462833,0.00014804587],"domain_scores_gemma":[0.9993859,0.000100893274,0.000052246105,0.00028660937,0.000084535044,0.00008980969],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004523282,0.00017880724,0.0001746334,0.00007343301,0.000049746013,0.000093826806,0.0005450395,0.000104974555,0.0003169858],"category_scores_gemma":[0.0000067478995,0.00015413627,0.00007665916,0.000020725096,0.000017704218,0.00009498143,0.00012482805,0.00017136545,0.00089408807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.659829e-7,0.000002898079,1.8767137e-8,0.0000020962834,0.000005602471,0.000016573153,0.0000053164367,0.00007019243,3.1083536e-7,0.6670081,0.0023748903,0.33051354],"study_design_scores_gemma":[0.00005109117,0.000036907248,0.000009108892,0.000023431166,0.000004666783,0.000012317205,1.8846404e-7,0.063448325,0.0000026613639,0.8456839,0.09053311,0.00019430563],"about_ca_topic_score_codex":0.000004516709,"about_ca_topic_score_gemma":0.0000017224797,"teacher_disagreement_score":0.33031923,"about_ca_system_score_codex":0.000024370578,"about_ca_system_score_gemma":0.00006998851,"threshold_uncertainty_score":0.99988383},"labels":[],"label_agreement":null},{"id":"W4243336095","doi":"10.1515/iupac.79.2087","title":"Synergism (in Toxicology)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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; Toxicology; Forensic toxicology; Computer science; Chemistry; Biology; Philosophy; Linguistics","score_opus":0.01731031097461969,"score_gpt":0.38881978752997437,"score_spread":0.37150947655535466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243336095","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015426473,0.00019060021,0.31446666,0.0011763795,0.0006119185,0.00009382092,0.6833694,0.000049932947,0.000025865691],"genre_scores_gemma":[0.000093936265,0.0001598786,0.007239162,0.0009573002,0.00040696672,0.000021742062,0.99098885,0.000014462787,0.00011767761],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974368,0.00009504653,0.00049330055,0.00066721905,0.00086570677,0.00044191044],"domain_scores_gemma":[0.9984322,0.00037560216,0.00014107305,0.0006313402,0.00027443972,0.00014533807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039027465,0.0003045261,0.00045594314,0.00028094643,0.00006665651,0.000092531445,0.0012008733,0.00025924324,0.000340749],"category_scores_gemma":[0.00039733108,0.00023362045,0.00009107923,0.00029250904,0.000076242686,0.00015058879,0.00045068143,0.00038666453,0.000006545113],"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":[0.00001641231,0.00012822793,0.0000012837704,0.00002943799,0.000017145449,0.00019408506,0.000006119405,0.00013696813,0.000002040318,0.0058161966,0.97649944,0.017152617],"study_design_scores_gemma":[0.00051796366,0.0001222576,0.000050595016,0.00020504522,0.000009734118,0.00001839036,0.0000014905863,0.0072576245,0.000002605612,0.032622293,0.9588611,0.0003308922],"about_ca_topic_score_codex":0.000060633974,"about_ca_topic_score_gemma":0.00022006832,"teacher_disagreement_score":0.30761948,"about_ca_system_score_codex":0.000295575,"about_ca_system_score_gemma":0.0007095605,"threshold_uncertainty_score":0.9526766},"labels":[],"label_agreement":null},{"id":"W4250827739","doi":"10.1515/iupac.81.0197","title":"Conceptual Model Diagram (in Ecological Risk Assessment)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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; Ecotoxicology; Relation (database); Environmental risk assessment; Ecology; Risk assessment; Computer science; Biology; Data mining","score_opus":0.02510147566771707,"score_gpt":0.41354697867271845,"score_spread":0.38844550300500136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250827739","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000658646,0.00006261653,0.4780245,0.00030645507,0.0002352697,0.00013042521,0.52109617,0.000053684897,0.000025016438],"genre_scores_gemma":[0.0018097685,0.0003428172,0.038720485,0.0006156435,0.0003216581,0.000049694274,0.9580679,0.000017860308,0.00005419255],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99630374,0.00023186828,0.00067439064,0.00091608893,0.0013042972,0.0005696131],"domain_scores_gemma":[0.9976441,0.00078112364,0.00027524887,0.00065413484,0.00042304452,0.00022232285],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070884137,0.00042195964,0.0006327834,0.00019527548,0.00013636088,0.00017070796,0.0013349194,0.00033629037,0.0003020008],"category_scores_gemma":[0.0006989937,0.000306509,0.00014852523,0.00025419847,0.00020447515,0.00024273436,0.0006296394,0.0008128165,0.0000040392965],"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":[0.000021821761,0.0003945524,0.00002542842,0.000013574687,0.000031290638,0.00010728091,0.000015277226,0.009178121,3.7984944e-7,0.018635273,0.9570821,0.014494891],"study_design_scores_gemma":[0.0009813623,0.00024098698,0.00029697322,0.00010307054,0.000027197539,0.0000069132875,0.0000059677764,0.6037173,3.5724452e-7,0.0803694,0.31376597,0.00048445884],"about_ca_topic_score_codex":0.0000656182,"about_ca_topic_score_gemma":0.00023268817,"teacher_disagreement_score":0.64331615,"about_ca_system_score_codex":0.00051677326,"about_ca_system_score_gemma":0.0014265244,"threshold_uncertainty_score":0.9999387},"labels":[],"label_agreement":null},{"id":"W4252598512","doi":"10.1007/978-1-4939-7131-2_100653","title":"Method of Simultaneous Linear Regressions","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical and Computational Modeling","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":"Econometrics; Linear regression; Statistics; Computer science; Mathematics","score_opus":0.04360409273759737,"score_gpt":0.32323076268271184,"score_spread":0.2796266699451145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252598512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.2254093e-7,0.00003999786,0.7144237,0.00007009338,0.00010882975,0.00004829768,0.000012217198,0.00005950523,0.28523698],"genre_scores_gemma":[0.00020933163,0.000007682275,0.72951376,0.0001654182,0.000111179485,8.968427e-7,0.0000053460294,0.000010452957,0.26997593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988573,0.0000146842085,0.00031331755,0.00035351716,0.00034264536,0.000118540374],"domain_scores_gemma":[0.9979776,0.0010987136,0.00014084087,0.00035164712,0.00034945272,0.000081748876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010288977,0.00016471275,0.00025763843,0.000085503365,0.00004902477,0.00001805175,0.00056264317,0.00013623486,0.0006906523],"category_scores_gemma":[0.000054459706,0.0001261438,0.0000942089,0.000032325133,0.000055782002,0.000043495635,0.0002901052,0.00014400529,0.00027186077],"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":[0.0000027123347,0.000006847726,3.2473366e-8,0.000011173237,0.00001887445,0.000020377789,0.0000390763,0.0017557702,0.000007317092,0.9124154,0.0008770968,0.08484531],"study_design_scores_gemma":[0.00003754402,0.000047837384,8.571643e-8,0.000058870603,0.000007678358,0.000015701073,5.4892547e-7,0.5576794,0.000048824157,0.4303081,0.011691379,0.000104012914],"about_ca_topic_score_codex":0.000004617096,"about_ca_topic_score_gemma":9.439627e-7,"teacher_disagreement_score":0.55592364,"about_ca_system_score_codex":0.00001264882,"about_ca_system_score_gemma":0.000085762746,"threshold_uncertainty_score":0.7562159},"labels":[],"label_agreement":null},{"id":"W4253322250","doi":"10.1515/iupac.81.0729","title":"Problem Formulation (in Ecological Risk Assessment)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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; Ecotoxicology; Environmental risk assessment; Relation (database); Ecology; Risk assessment; Computer science; Biology; Data mining; Linguistics","score_opus":0.0177922433989732,"score_gpt":0.40432736864352,"score_spread":0.3865351252445468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253322250","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000040034272,0.000030038273,0.43964443,0.00037190862,0.00017815897,0.00017152242,0.5594842,0.00004556567,0.000034151275],"genre_scores_gemma":[0.00083447876,0.00014018035,0.053145636,0.00022250004,0.00026248253,0.00003707756,0.94531584,0.00001166247,0.000030131585],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970945,0.00017294721,0.0006104126,0.0006688781,0.0010463019,0.00040697635],"domain_scores_gemma":[0.99816895,0.00055404217,0.00029163266,0.00046280093,0.00039706283,0.00012550692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007630706,0.00030196135,0.00042547166,0.00021980095,0.00012303199,0.00016705474,0.00080907444,0.00025028404,0.0002842906],"category_scores_gemma":[0.00034278128,0.00021604377,0.00009907147,0.0002804071,0.00003788299,0.0003029225,0.0003808063,0.0005178707,0.0000031316279],"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":[0.000022503053,0.00031309496,0.00008036307,0.000043661214,0.000023296276,0.00005920197,0.0000074498016,0.0010591142,6.306973e-7,0.009115244,0.9489688,0.040306628],"study_design_scores_gemma":[0.000912523,0.00028242165,0.002666468,0.00018324661,0.00002239915,0.000008749091,0.0000019079205,0.09940298,5.065237e-7,0.16166157,0.73445415,0.00040305255],"about_ca_topic_score_codex":0.0000740515,"about_ca_topic_score_gemma":0.00030160128,"teacher_disagreement_score":0.38649878,"about_ca_system_score_codex":0.0005274006,"about_ca_system_score_gemma":0.0007526392,"threshold_uncertainty_score":0.88100094},"labels":[],"label_agreement":null},{"id":"W4312074816","doi":"","title":"Cartes de contrôle non-paramétriques adaptées à des distributions asymétriques et fondées sur la statistique des prédécesseurs.","year":2013,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"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":"Physics","score_opus":0.02788071387989953,"score_gpt":0.2663927286626205,"score_spread":0.23851201478272094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312074816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07166601,0.0043924344,0.90038276,0.010309997,0.00020317662,0.00069095154,0.00067725376,0.00041440732,0.011262995],"genre_scores_gemma":[0.5224297,0.0020921784,0.47003064,0.00013743177,0.000037246387,0.00026411476,0.00054587907,0.00005012481,0.00441267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.985002,0.010642304,0.001161092,0.0014338981,0.0007237904,0.0010369457],"domain_scores_gemma":[0.9745815,0.014502218,0.0007328828,0.001612617,0.007927645,0.00064316025],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006218442,0.0007886943,0.0008192951,0.00021973258,0.0011461254,0.0028194035,0.0023764654,0.0004938695,0.00013949058],"category_scores_gemma":[0.006439,0.00084185146,0.00034446025,0.0007128851,0.0016548346,0.0012080661,0.0019096629,0.0011600446,0.00009387359],"study_design_candidate":"theoretical_or_conceptual","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":[0.00001331063,0.00081750617,0.0020587675,0.0003363412,0.00014169833,0.000026613656,0.011454614,0.0024688244,0.00056477793,0.8716582,0.0017901623,0.108669154],"study_design_scores_gemma":[0.00050844054,0.0000037899354,0.03206808,0.0032964984,0.00007532493,0.000060716204,0.000111087495,0.50021076,0.010716143,0.4460277,0.0061390935,0.00078235357],"about_ca_topic_score_codex":0.023496026,"about_ca_topic_score_gemma":0.0062170476,"teacher_disagreement_score":0.49774194,"about_ca_system_score_codex":0.00042832192,"about_ca_system_score_gemma":0.0017311429,"threshold_uncertainty_score":0.99940324},"labels":[],"label_agreement":null},{"id":"W4378808490","doi":"10.20535/s0021347022100041","title":"Метод матричних d-дерев і його застосування до символьного аналізу лінійних параметричних кіл у частотній області","year":2022,"lang":"uk","type":"article","venue":"Известия высших учебных заведений Радиоэлектроника","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"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":"MATLAB; Computer science; Mathematics; Geology; Programming language","score_opus":0.02626871104852396,"score_gpt":0.24129683151507955,"score_spread":0.2150281204665556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378808490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05125106,0.012387525,0.8175761,0.025829514,0.025653891,0.0050136405,0.003359754,0.0039614094,0.0549671],"genre_scores_gemma":[0.9153405,0.00046666025,0.04719568,0.011644101,0.0036451565,0.0011430793,0.00081686914,0.00067299156,0.019074995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9745536,0.0024249468,0.0045665964,0.006069713,0.0071520926,0.0052330634],"domain_scores_gemma":[0.9869124,0.0028385075,0.0019101639,0.004556632,0.0012419395,0.0025403379],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","open_science","insufficient_payload"],"category_scores_codex":[0.0036902537,0.0031210424,0.0031383305,0.0017488422,0.005522323,0.002317095,0.009067157,0.0008773056,0.012420887],"category_scores_gemma":[0.0008186463,0.0035025575,0.001800981,0.005121874,0.0010502873,0.0023275928,0.008777856,0.004845431,0.005985452],"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":[0.001039119,0.0042196065,0.0030694886,0.00075027486,0.0013949804,0.0024581936,0.0072116544,0.09208215,0.0010921264,0.6084154,0.14351599,0.134751],"study_design_scores_gemma":[0.0062048654,0.0024098274,0.005422874,0.000389392,0.00065115327,0.0013510074,0.0016546311,0.6019432,0.00056815153,0.15572435,0.217513,0.0061675753],"about_ca_topic_score_codex":0.001827782,"about_ca_topic_score_gemma":0.00017321923,"teacher_disagreement_score":0.8640894,"about_ca_system_score_codex":0.0019407412,"about_ca_system_score_gemma":0.0037832623,"threshold_uncertainty_score":0.99923897},"labels":[],"label_agreement":null},{"id":"W4381273585","doi":"10.5281/zenodo.8058359","title":"sadsa","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Adidas (Canada)","funders":"","keywords":"Computer science","score_opus":0.05767832909703348,"score_gpt":0.26457545358491935,"score_spread":0.2068971244878859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381273585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029381367,0.000015387173,0.9395453,0.0020560848,0.00012365707,0.000114604016,0.00003264836,0.00226128,0.052912906],"genre_scores_gemma":[0.9837245,0.000026724192,0.013290103,0.0005183586,0.00016789218,3.6537482e-8,0.00062506564,0.00058852136,0.0010588407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890953,0.00010414938,0.00013442735,0.00029399697,0.00030826908,0.00024959765],"domain_scores_gemma":[0.9992689,0.000048075228,0.000030067531,0.0002667044,0.00027360106,0.000112689806],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00034242572,0.00006789071,0.000065407636,0.00016125735,0.0012366612,0.00068010285,0.00111284,0.000022797818,0.0011752436],"category_scores_gemma":[0.00029549169,0.000069419555,0.00002706089,0.00087965594,0.000043296743,0.00022517913,0.0012280121,0.000106925276,0.023747],"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":[0.000005555501,0.000032900436,7.46584e-7,0.000014613401,0.000011656378,0.000023445842,0.00051255507,0.0014836149,0.00051959633,0.43222615,0.14390907,0.42126012],"study_design_scores_gemma":[0.00019592729,0.000078709934,0.0007829801,0.0000104488645,0.00000208029,0.000049431637,0.000044490123,0.23059776,0.000079141464,0.029925652,0.73810196,0.00013143943],"about_ca_topic_score_codex":0.0000032818293,"about_ca_topic_score_gemma":1.9605421e-8,"teacher_disagreement_score":0.9807863,"about_ca_system_score_codex":0.00003595609,"about_ca_system_score_gemma":0.0000022814702,"threshold_uncertainty_score":0.9997378},"labels":[],"label_agreement":null},{"id":"W4387673732","doi":"10.5539/ijc.v15n2p34","title":"Using Buswell’s Equation to Count Quantity of Biomethane in Organochlorine Compounds","year":2023,"lang":"en","type":"article","venue":"International Journal of Chemistry","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":12,"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":"Chemistry; Biogas; Anaerobic digestion; Methane; Pulp and paper industry; Environmental chemistry; Organic chemistry; Waste management","score_opus":0.09043296997110498,"score_gpt":0.3561548231351606,"score_spread":0.2657218531640556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387673732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5066456,0.000012765283,0.49241656,0.0005228229,0.0002915212,0.000013932539,0.0000049652967,0.0000074161244,0.00008441574],"genre_scores_gemma":[0.9576319,0.000007838159,0.04212138,0.00006695558,0.00014396005,5.4177826e-7,0.0000049332984,0.000003923893,0.000018608876],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986102,0.00001433319,0.0004837481,0.00011185749,0.00067906646,0.0001008195],"domain_scores_gemma":[0.9987165,0.0001987817,0.00022874709,0.00008018769,0.0007134502,0.00006237785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038577805,0.000067023015,0.00014315118,0.0002294044,0.000013366349,0.000045425484,0.0006350975,0.000029803929,0.000019363084],"category_scores_gemma":[0.00025760042,0.00006542568,0.0000471222,0.00056233787,0.000019106003,0.00019453057,0.00014428831,0.0000963445,0.000008039283],"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":[0.00014147737,0.00022777739,0.004780958,0.000061570194,0.00011324482,0.0002269252,0.0005071471,0.19783469,0.7716678,0.015808025,0.00023048975,0.008399925],"study_design_scores_gemma":[0.0010569934,0.000063178595,0.008364298,0.00029532224,0.000009029326,0.00020790234,0.00007228638,0.7150161,0.22757764,0.04691783,0.00021415626,0.00020525811],"about_ca_topic_score_codex":0.000037106493,"about_ca_topic_score_gemma":0.000001170283,"teacher_disagreement_score":0.54409015,"about_ca_system_score_codex":0.0001173036,"about_ca_system_score_gemma":0.00014057779,"threshold_uncertainty_score":0.2667982},"labels":[],"label_agreement":null},{"id":"W4388652405","doi":"10.48550/arxiv.1702.06510","title":"Algorithmes de classification et d'optimisation: participation du\\n LIA/ADOC \\\\'a DEFT'14","year":2017,"lang":"fr","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"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":"Humanities; Political science; Philosophy","score_opus":0.23569268158592227,"score_gpt":0.2579987223630974,"score_spread":0.02230604077717513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388652405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04495804,0.000081447426,0.9449827,0.0036567638,0.0010587554,0.0002856543,0.000036155692,0.00016356003,0.00477689],"genre_scores_gemma":[0.9402813,0.00035095448,0.055229716,0.00045379662,0.00030893917,0.000006396394,0.00009735524,0.000022385146,0.0032491887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971469,0.0004434135,0.00040523036,0.0012594302,0.00022558597,0.00051946123],"domain_scores_gemma":[0.996542,0.00055500516,0.00057375507,0.0009924577,0.0009936813,0.0003431283],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006640018,0.000395392,0.00035488658,0.00015130117,0.0007150249,0.0006008078,0.0013736746,0.00035262946,0.00009303809],"category_scores_gemma":[0.00043344926,0.0004921654,0.00021150615,0.0002847075,0.0002736697,0.0009960487,0.00079257536,0.0005596062,0.0003503356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021686734,0.0001094117,0.0019368147,0.000033617933,0.000045432193,0.0000766307,0.00042799767,0.4331765,0.00000997121,0.55514497,0.0002270353,0.008789937],"study_design_scores_gemma":[0.00034512862,0.000052133957,0.050053228,0.00011145638,0.00010320829,0.000010595521,0.00004203646,0.70265955,0.000013764763,0.24509859,0.00114215,0.0003681698],"about_ca_topic_score_codex":0.00043942014,"about_ca_topic_score_gemma":0.00006028152,"teacher_disagreement_score":0.8953232,"about_ca_system_score_codex":0.00045318436,"about_ca_system_score_gemma":0.0006955375,"threshold_uncertainty_score":0.999753},"labels":[],"label_agreement":null},{"id":"W4388926794","doi":"","title":"Menace du stéréotype de conduite automobile. Rapport final sur convention DSR","year":2022,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Statistical and Computational Modeling","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 Sherbrooke","funders":"","keywords":"Convention; Art; Political science; Law","score_opus":0.04359278346874368,"score_gpt":0.25194004821473115,"score_spread":0.20834726474598747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388926794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040479697,0.0013397227,0.9123528,0.020471266,0.001084441,0.0005757364,0.00014616275,0.0003542524,0.023195924],"genre_scores_gemma":[0.6701826,0.00063134823,0.25994182,0.0007333343,0.00011195788,0.00031492044,0.0010613403,0.00008365644,0.06693908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9868738,0.008600415,0.0010945618,0.0015315238,0.001095279,0.0008044316],"domain_scores_gemma":[0.98949075,0.00385517,0.00084190496,0.0019176698,0.0034319826,0.00046253554],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0094452845,0.0005700966,0.0006053828,0.0002291885,0.0010401281,0.0009307934,0.0026822907,0.00030176376,0.0020330476],"category_scores_gemma":[0.001789156,0.0006883428,0.0004045132,0.0007811838,0.00039622586,0.00044227554,0.0034137918,0.0012387941,0.00019726677],"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":[0.000025955196,0.00091837964,0.0018423087,0.00017507584,0.00012622717,0.00006367451,0.006466649,0.009668094,0.0003334063,0.91491663,0.0030702357,0.062393337],"study_design_scores_gemma":[0.0008423638,0.0000027494018,0.010361539,0.0007477459,0.00008107168,0.00009730688,0.00011038047,0.85642606,0.0011697655,0.061663117,0.06780293,0.00069496955],"about_ca_topic_score_codex":0.0024669485,"about_ca_topic_score_gemma":0.00031818307,"teacher_disagreement_score":0.85325354,"about_ca_system_score_codex":0.00048149884,"about_ca_system_score_gemma":0.0020847816,"threshold_uncertainty_score":0.9995568},"labels":[],"label_agreement":null},{"id":"W4390390233","doi":"10.1093/jrsssb/qkad149","title":"Ivor Cribben and Anastasiou Andreas’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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":"Office of Defense Programs; Multidisciplinary University Research Initiative; Engineering and Physical Sciences Research Council; Defence Science and Technology Laboratory; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Probabilistic logic; Computer science; Cognitive science; Psychology; Artificial intelligence","score_opus":0.03714742790316448,"score_gpt":0.3061403542939992,"score_spread":0.2689929263908347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390390233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008636775,0.00007431837,0.97441536,0.015294802,0.00048247827,0.00041502767,0.0006156438,0.000026197647,0.000039383453],"genre_scores_gemma":[0.64466804,0.000026106867,0.35474083,0.00032996817,0.000100266974,0.000011528134,0.000012850021,0.000020609981,0.000089810426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994652,0.0022340263,0.0011274896,0.0004558905,0.0010373315,0.00049328984],"domain_scores_gemma":[0.9864369,0.011904765,0.00058643916,0.00032331416,0.00043047953,0.00031807987],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003146126,0.0003076124,0.00075007824,0.000058017704,0.0006224083,0.00010018205,0.00073291163,0.00013773407,0.000029663759],"category_scores_gemma":[0.022104738,0.000121384524,0.00016256016,0.000600744,0.0010634168,0.000108274915,0.0009273543,0.0008280754,0.0000028507075],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054560474,0.0001362029,0.001021745,0.00021333247,0.00013319294,0.000021339125,0.0010373404,0.0130886305,0.00042271925,0.93903077,0.0022645558,0.04208457],"study_design_scores_gemma":[0.000848446,0.0014449381,0.1013371,0.00032141813,0.00021888468,0.00008200861,0.00036261772,0.37670726,0.0002924651,0.51732934,0.00078634365,0.00026919396],"about_ca_topic_score_codex":0.00006532084,"about_ca_topic_score_gemma":0.000021887561,"teacher_disagreement_score":0.63603127,"about_ca_system_score_codex":0.00008165968,"about_ca_system_score_gemma":0.00016077296,"threshold_uncertainty_score":0.9861325},"labels":[],"label_agreement":null},{"id":"W4391853976","doi":"10.2139/ssrn.4728247","title":"Machine Learning-Based Platform for the Identification of Critical Generators - Context of High Renewable Integration","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Statistical and Computational Modeling","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":"Identification (biology); Context (archaeology); Renewable energy; Computer science; Artificial intelligence; Engineering; Geology; Electrical engineering; Biology","score_opus":0.020803999646728445,"score_gpt":0.28237359169512505,"score_spread":0.2615695920483966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391853976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032445544,0.004534892,0.98901135,0.0021764706,0.0007698492,0.00018948059,0.000037788515,0.000026567208,0.00000903793],"genre_scores_gemma":[0.98980457,0.00021661953,0.00959732,0.000046382254,0.00017136945,0.000030606865,0.00003787402,0.000014718226,0.000080560654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979616,0.000068611356,0.0007514385,0.00028234572,0.00039575482,0.00054028863],"domain_scores_gemma":[0.99797386,0.0008157183,0.00037994192,0.00021702026,0.0005746094,0.000038848022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017877303,0.00015977395,0.00024918158,0.00014597301,0.00016170497,0.00015177655,0.00066610874,0.00010623856,0.0000035746516],"category_scores_gemma":[0.00049600506,0.000111772715,0.00018629794,0.00016167332,0.00007833474,0.00008382581,0.00018095553,0.0015828578,0.0000020243886],"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":[0.000028361219,0.0000365484,0.0000026187784,0.000074072355,0.000074460026,3.1604753e-7,0.00009511151,0.19509472,0.00024136592,0.76347184,0.00001866188,0.04086191],"study_design_scores_gemma":[0.0001016019,0.00011027497,0.0000041130547,0.000043882774,0.000044540084,0.000008356008,0.000059231992,0.5020217,0.0021062305,0.49543008,0.000013792684,0.00005623069],"about_ca_topic_score_codex":0.00036489803,"about_ca_topic_score_gemma":0.00020452893,"teacher_disagreement_score":0.98656,"about_ca_system_score_codex":0.0002872072,"about_ca_system_score_gemma":0.0023505006,"threshold_uncertainty_score":0.68768173},"labels":[],"label_agreement":null},{"id":"W4392305885","doi":"10.2139/ssrn.4723580","title":"Assessing Monotonicity: An Approach Based on Transformed Order Statistics","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical and Computational Modeling","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":"Monotonic function; Statistics; Order statistic; Order (exchange); Mathematics; Econometrics; Computer science; Economics; Mathematical analysis","score_opus":0.021915282977978814,"score_gpt":0.2982880466852278,"score_spread":0.276372763707249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392305885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019856708,0.00030311756,0.9955082,0.0007856716,0.0002504736,0.0000794544,0.000005512289,0.00013650108,0.00094542716],"genre_scores_gemma":[0.71232426,0.00005204242,0.28698638,0.00040323992,0.0001427726,0.0000063883567,0.000011963506,0.000017576236,0.000055404875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99745315,0.00010242734,0.0002836655,0.00036257657,0.0005196089,0.0012785443],"domain_scores_gemma":[0.9992606,0.00023836824,0.00004009694,0.00016333743,0.00016737623,0.00013020623],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010363818,0.00016741954,0.00014550968,0.00017159269,0.00025437,0.0010478706,0.0004758023,0.000052785406,0.000008833789],"category_scores_gemma":[0.00005293577,0.00013697892,0.000057101723,0.00041839128,0.000027189164,0.00078074884,0.0000185816,0.001369738,0.000018841636],"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":[0.000007145574,0.00006835527,0.0000034072004,0.000010083004,0.000023467232,0.000011951197,0.000072035495,0.042086314,0.000015214073,0.71242124,0.000014266927,0.24526653],"study_design_scores_gemma":[0.00014795488,0.00016102618,0.000035745903,0.000017472028,0.00000964743,0.00009434373,0.000074164214,0.5954907,0.000007398262,0.40376705,0.000085188876,0.00010931846],"about_ca_topic_score_codex":0.000013029387,"about_ca_topic_score_gemma":0.000010939367,"teacher_disagreement_score":0.71033853,"about_ca_system_score_codex":0.00044664252,"about_ca_system_score_gemma":0.0036891964,"threshold_uncertainty_score":0.99998915},"labels":[],"label_agreement":null},{"id":"W4393234555","doi":"10.5206/mase/16981","title":"Time-delayed models for the effects of toxicants on populations in contaminated aquatic ecosystems","year":2024,"lang":"en","type":"article","venue":"Mathematics in Applied Sciences and Engineering","topic":"Statistical and Computational Modeling","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":"Ecosystem; Aquatic ecosystem; Contamination; Environmental science; Ecology; Biology; Environmental chemistry; Chemistry","score_opus":0.030970958800309603,"score_gpt":0.26419293148372114,"score_spread":0.23322197268341155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393234555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048025988,0.00018235666,0.9511994,0.00005729469,0.000102477265,0.00024891098,0.0000014616535,0.00002594017,0.00015616335],"genre_scores_gemma":[0.9238077,0.0000038181447,0.07610861,0.000008387098,0.0000067212636,0.00005598399,3.5910733e-7,0.000003395048,0.000005047949],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994056,0.0000041576127,0.00019642827,0.00014173685,0.00013580792,0.000116256095],"domain_scores_gemma":[0.99828994,0.0015991485,0.000019130544,0.000068964946,0.000005532589,0.00001730514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035360552,0.00006415785,0.00011102903,0.000117720454,0.000038045993,0.00006701398,0.00018163165,0.00001811181,3.3429845e-7],"category_scores_gemma":[0.000046580626,0.00004355156,0.000013710657,0.00034135918,0.000022798611,0.000055835117,0.00003181427,0.000049528775,0.0000011169893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.217918e-7,0.000009638185,5.1009596e-7,0.00014248198,0.0000021281332,5.036108e-7,0.00047801743,0.38874805,0.00031276114,0.608227,0.0000019494,0.002076535],"study_design_scores_gemma":[0.0000727434,0.00002990594,0.000016284856,0.00021804376,0.0000023852854,9.533823e-7,0.00003115612,0.87274367,0.000118583805,0.12671778,0.0000013107646,0.00004716521],"about_ca_topic_score_codex":0.000008475914,"about_ca_topic_score_gemma":0.000005879315,"teacher_disagreement_score":0.8757817,"about_ca_system_score_codex":0.00001556174,"about_ca_system_score_gemma":0.000013748516,"threshold_uncertainty_score":0.17759812},"labels":[],"label_agreement":null},{"id":"W4394174006","doi":"10.6084/m9.figshare.21375534.v2","title":"BIOL3250 -- BIOBLITZ (DATA &amp; METADATA FOR FIELD EXPERIMENT).xlsx","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Statistical and Computational Modeling","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":"Metadata; Field (mathematics); Computer science; World Wide Web; Mathematics","score_opus":0.24182116681038116,"score_gpt":0.3799860195009268,"score_spread":0.13816485269054565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394174006","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5830391e-8,0.0008138567,0.0716274,0.0003260552,0.00037827992,0.0003549236,0.92639005,0.00008490458,0.00002453337],"genre_scores_gemma":[5.731894e-7,0.000008494164,0.024966065,0.0015214343,0.000246726,0.00065803115,0.9723934,0.000014861823,0.00019039615],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976502,0.00005924452,0.00036223224,0.0010722198,0.00050067966,0.00035542087],"domain_scores_gemma":[0.99581766,0.0013817435,0.0001843296,0.002366541,0.0001206005,0.00012911683],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00011014468,0.00029663063,0.00031323152,0.00013404613,0.0002578635,0.00055006513,0.005694825,0.00013951324,0.30236652],"category_scores_gemma":[0.0017756338,0.00028551015,0.000105980165,0.00028734354,0.0000048649104,0.0005746925,0.005118786,0.00031427588,0.0007838198],"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":[0.000005802755,0.000058625883,9.555554e-9,0.00014500748,0.000040514253,0.000009449738,0.0000064719165,0.00003238716,4.8221665e-7,0.00036183023,0.9958417,0.0034977568],"study_design_scores_gemma":[0.00018025449,0.00006735096,3.2568227e-7,0.00015372592,0.000017545088,0.00000828615,0.0000027555511,0.011751176,0.000009640293,0.0013298492,0.98611397,0.0003651309],"about_ca_topic_score_codex":0.00006476166,"about_ca_topic_score_gemma":0.000019037421,"teacher_disagreement_score":0.3015827,"about_ca_system_score_codex":0.00004216173,"about_ca_system_score_gemma":0.00024466886,"threshold_uncertainty_score":0.99999416},"labels":[],"label_agreement":null},{"id":"W4395449262","doi":"10.3103/s1066530724700054","title":"Assessing Monotonicity: An Approach Based on Transformed Order Statistics","year":2024,"lang":"en","type":"article","venue":"Mathematical Methods of Statistics","topic":"Statistical and Computational Modeling","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":"Western University","funders":"","keywords":"Monotonic function; Mathematics; Order (exchange); Order statistic; Applied mathematics; Statistics; Econometrics; Mathematical economics; Mathematical optimization; Economics; Mathematical analysis","score_opus":0.09185669131148919,"score_gpt":0.42350190687021516,"score_spread":0.33164521555872595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395449262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000065804255,0.00003489174,0.996078,0.00019341442,0.00019245027,0.00025008764,0.00027832057,0.00021043731,0.002696591],"genre_scores_gemma":[0.017206706,0.0000030238975,0.98226714,0.0003329846,0.000038821858,0.000036971764,0.000050785082,0.000037043657,0.000026510314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708205,0.00039908945,0.00079102797,0.00058032613,0.00076382863,0.00038367228],"domain_scores_gemma":[0.99209154,0.006711849,0.000094391595,0.00047074366,0.00040298345,0.000228495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014611419,0.00028764355,0.0004910325,0.00020940258,0.000113591464,0.00054389343,0.0005862153,0.00010343677,0.00007762186],"category_scores_gemma":[0.0013521995,0.00023690079,0.00006339493,0.0005879078,0.0001568048,0.00040122494,0.00006690681,0.00033237805,0.000025021725],"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":[0.000006623722,0.0002477039,5.861977e-7,0.0005437211,0.000021633281,0.000017538121,0.00022465317,0.008695042,0.00008199706,0.71635747,0.000086561115,0.27371648],"study_design_scores_gemma":[0.00010894104,0.000115063245,0.00002410508,0.00008152551,0.000029942346,0.000006079366,0.000032070537,0.5418516,0.00018205149,0.4573706,0.00004570006,0.0001523378],"about_ca_topic_score_codex":0.000005122169,"about_ca_topic_score_gemma":2.3399505e-7,"teacher_disagreement_score":0.5331565,"about_ca_system_score_codex":0.000055012784,"about_ca_system_score_gemma":0.00030530422,"threshold_uncertainty_score":0.96605337},"labels":[],"label_agreement":null},{"id":"W4398233673","doi":"10.1093/ndt/gfae069.831","title":"#782 Latent profiles of patients undergoing maintenance hemodialysis based on hemodynamic indicators","year":2024,"lang":"en","type":"article","venue":"Nephrology Dialysis Transplantation","topic":"Statistical and Computational Modeling","field":"Computer Science","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":"Montreal Heart Institute; McGill University Health Centre; McGill University","funders":"","keywords":"Hemodynamics; Hemodialysis; Medicine; Intensive care medicine; Internal medicine; Cardiology","score_opus":0.00780697178200974,"score_gpt":0.2236235981520679,"score_spread":0.21581662637005816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398233673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27094728,0.000022575998,0.72805136,0.00041983553,0.00015500397,0.000116964446,0.000045793015,0.00010922233,0.00013193162],"genre_scores_gemma":[0.97594684,0.000037538386,0.023054598,0.0006274058,0.00002903485,0.000036929967,0.00024507174,0.000011770751,0.0000108114755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834275,0.00013298092,0.00040731012,0.00048823646,0.00039490598,0.0002338003],"domain_scores_gemma":[0.9987181,0.00083072396,0.00009771326,0.00020660047,0.00008523272,0.000061628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018956908,0.00016838276,0.00024749563,0.0005705688,0.000083263534,0.000054608754,0.00030136295,0.00009653004,0.000019370973],"category_scores_gemma":[0.000028265857,0.0001478393,0.00014984728,0.0008406275,0.00006739127,0.00020197699,0.000016830858,0.00013471651,0.000029805837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004129925,0.0005925224,0.021099804,0.000737072,0.00045059656,0.00009056034,0.0017915145,0.57212996,0.001074948,0.24866785,0.00014575606,0.15280643],"study_design_scores_gemma":[0.0005865303,0.00016047175,0.044597015,0.00007366057,0.00013805565,0.0000013952792,0.000003083941,0.94474334,0.00049716985,0.009022415,0.000025405541,0.00015149206],"about_ca_topic_score_codex":0.000014184218,"about_ca_topic_score_gemma":0.000015094709,"teacher_disagreement_score":0.70499957,"about_ca_system_score_codex":0.000058073325,"about_ca_system_score_gemma":0.00011765173,"threshold_uncertainty_score":0.6028712},"labels":[],"label_agreement":null},{"id":"W4398424321","doi":"10.7910/dvn/bki1x3/wrfwci","title":"functions.R","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":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":"Geology; Geography","score_opus":0.029273647415328816,"score_gpt":0.25494285721183835,"score_spread":0.22566920979650953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398424321","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.7090805e-7,5.3668884e-7,0.40235007,0.000008502563,0.0010450858,0.00007080076,0.5963526,0.000040311035,0.00013193153],"genre_scores_gemma":[0.000004070354,0.000020052714,0.013887625,0.0008021885,0.00018406089,0.00001271573,0.9845209,0.0000067212827,0.00056166865],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849796,0.00004547322,0.0002384699,0.0005559943,0.00042334475,0.00023874464],"domain_scores_gemma":[0.9981986,0.00020197166,0.000099574325,0.0012978101,0.00009747358,0.00010456074],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00011676635,0.0002057651,0.00021496316,0.00013163089,0.00009389183,0.00022274902,0.0013678705,0.00012770988,0.004981533],"category_scores_gemma":[0.00009806416,0.00019377925,0.00007637719,0.00019237882,0.000027428776,0.00036696694,0.0007881721,0.0002855981,0.46020293],"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":[0.0000035870069,0.00003236749,3.1534117e-7,0.00003392401,0.000022927,0.000029884217,0.0000026281837,0.00048646642,4.529616e-7,0.0029639872,0.9950922,0.0013312299],"study_design_scores_gemma":[0.00014811935,0.00003037895,0.00000882885,0.000028403721,0.000023109884,0.000017398206,0.0000024876517,0.032192346,4.3441153e-7,0.0013126478,0.9660029,0.0002329668],"about_ca_topic_score_codex":0.00006761085,"about_ca_topic_score_gemma":0.000005040051,"teacher_disagreement_score":0.45522138,"about_ca_system_score_codex":0.000042778764,"about_ca_system_score_gemma":0.0001612976,"threshold_uncertainty_score":0.99592805},"labels":[],"label_agreement":null},{"id":"W4399032500","doi":"10.32388/vdf43o","title":"Review of: \"Intersections of Statistical Significance and Substantive Significance: Pearson’s Correlation Coefficients Under a Known True Null Hypothesis\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Statistical and Computational Modeling","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":"Statistical significance; Null (SQL); Null hypothesis; Statistics; Mathematics; Pearson product-moment correlation coefficient; Statistical hypothesis testing; Correlation; Data mining; Computer science; Geometry","score_opus":0.04589090352085184,"score_gpt":0.3138652352318848,"score_spread":0.26797433171103296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399032500","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000040740756,0.114735685,0.87509847,0.0052478444,0.0012176886,0.00079805276,0.0010289523,0.000068135625,0.0018011044],"genre_scores_gemma":[0.068078116,0.3413969,0.46646425,0.019892346,0.0007426397,0.00092872867,0.0019307745,0.0003147299,0.10025152],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99653375,0.00024019033,0.0011824534,0.00090997026,0.00084167096,0.00029198476],"domain_scores_gemma":[0.99585605,0.0021979662,0.000458138,0.0004475472,0.00089341716,0.00014689789],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005864546,0.00038921542,0.0010396712,0.00019378915,0.000073261814,0.00005926722,0.0005387693,0.00014090487,0.0003198265],"category_scores_gemma":[0.0005914388,0.00032835832,0.00019018694,0.0008107815,0.00024570935,0.00011881435,0.00020734253,0.000446326,0.00006390224],"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":[0.00001971054,0.00023061197,0.0000026309124,0.06242499,0.0002132826,0.000015591622,0.00018235714,0.00091536954,0.000017629782,0.30687878,0.5636572,0.06544186],"study_design_scores_gemma":[0.00063135556,0.00068500114,0.00034635616,0.21132046,0.001483322,0.00008807783,0.00011281862,0.55590177,0.000063627485,0.09003395,0.13769671,0.0016365442],"about_ca_topic_score_codex":0.0001821273,"about_ca_topic_score_gemma":0.000030850802,"teacher_disagreement_score":0.5549864,"about_ca_system_score_codex":0.0001295892,"about_ca_system_score_gemma":0.0005207061,"threshold_uncertainty_score":0.99991685},"labels":[],"label_agreement":null},{"id":"W4399728835","doi":"10.52202/074122-0040","title":"Observing the Interaction of Kolmogorov Length Scales in Lab and Industrial Scale Continuous Flow Loops","year":2024,"lang":"en","type":"article","venue":"","topic":"Statistical and Computational Modeling","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":"Scale (ratio); Length scale; Continuous flow; Flow (mathematics); Computer science; Statistical physics; Mechanics; Physics; Geography; Cartography","score_opus":0.03958880974101665,"score_gpt":0.2709888673852195,"score_spread":0.23140005764420285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399728835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30510056,0.00020676565,0.69205785,0.0014138365,0.00040765986,0.00007385747,0.0000027111682,0.00004887336,0.00068790966],"genre_scores_gemma":[0.9785723,0.000011379929,0.02120238,0.00008713416,0.00007018548,0.0000043247946,0.0000011122605,0.0000029765406,0.000048179467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934804,0.00004615494,0.00019015302,0.00018275222,0.00013789473,0.00009501969],"domain_scores_gemma":[0.9992503,0.00057671376,0.000019330233,0.0000919043,0.000036966438,0.000024820289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020915514,0.000060313796,0.00009187512,0.000053278858,0.000034233184,0.00014079892,0.00018483805,0.000031436943,0.000008987515],"category_scores_gemma":[0.000051990894,0.000039535666,0.000020153793,0.00022408536,0.000033090804,0.00024405886,0.00014736377,0.00013811409,0.00000579531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000131569095,0.00003681143,0.0017617593,0.00002960342,0.0000146775965,0.000010918921,0.00083392713,0.006032239,0.00021752679,0.1661908,0.00037594992,0.8244826],"study_design_scores_gemma":[0.00012276632,0.000027417715,0.0023121196,0.00007759233,0.0000027696042,0.000010514221,0.000127814,0.9724026,0.00019227812,0.024534268,0.00013733565,0.000052535393],"about_ca_topic_score_codex":0.00014494901,"about_ca_topic_score_gemma":0.00008456187,"teacher_disagreement_score":0.96637034,"about_ca_system_score_codex":0.0000144438345,"about_ca_system_score_gemma":0.00002810731,"threshold_uncertainty_score":0.16122176},"labels":[],"label_agreement":null},{"id":"W4402836358","doi":"10.2139/ssrn.4967592","title":"Oriented Data-Generating Processes: A Categorization of ROC Curves","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Statistical and Computational Modeling","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 Laval","funders":"","keywords":"Categorization; Computer science; Receiver operating characteristic; Natural language processing; Artificial intelligence; Data mining; Machine learning","score_opus":0.027945134908309185,"score_gpt":0.2940385003390201,"score_spread":0.26609336543071094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402836358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001272666,0.023538103,0.9731998,0.0009592457,0.0006850724,0.00013978599,0.000036543734,0.000076480705,0.00009233731],"genre_scores_gemma":[0.9240968,0.010554312,0.06355778,0.00020975381,0.00086202245,0.000021462607,0.00039813836,0.000042095224,0.00025762877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972118,0.000086285356,0.00060869515,0.00058084406,0.00059548044,0.0009168522],"domain_scores_gemma":[0.99836737,0.00011990744,0.0003632812,0.000470385,0.000612052,0.000067012734],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0012370002,0.00021646189,0.00028418362,0.00015842234,0.000117681484,0.0002066673,0.0016306317,0.00009282787,0.0000036873498],"category_scores_gemma":[0.0003677075,0.00018620068,0.00006145555,0.0004887545,0.000025213789,0.00025320533,0.0018741661,0.0023045766,0.000009465181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070381952,0.0000677924,0.000017998214,0.0012036999,0.00025054146,0.000008046908,0.00026581183,0.035061546,0.00005477184,0.9250655,0.00026294723,0.03773428],"study_design_scores_gemma":[0.000060578393,0.00004535385,0.0000018804969,0.00045766655,0.000039209426,0.00008449097,0.000038787955,0.4648269,0.000022939215,0.53426486,0.00003824399,0.00011906661],"about_ca_topic_score_codex":0.000052285053,"about_ca_topic_score_gemma":0.00009876907,"teacher_disagreement_score":0.92282414,"about_ca_system_score_codex":0.00028331517,"about_ca_system_score_gemma":0.01089263,"threshold_uncertainty_score":0.99999714},"labels":[],"label_agreement":null},{"id":"W4403221414","doi":"10.5539/ijsp.v13n3p48","title":"Comparison of Test Statistics for Testing the Regression Coefficients in the Ridge, Liu and Kibria-Lukman Logistic Regression Models: Simulation and Application","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical and Computational Modeling","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":"Statistics; Logistic regression; Mathematics; Ridge; Regression analysis; Regression testing; Regression; Test (biology); Econometrics; Statistical hypothesis testing; Computer science; Geography; Geology","score_opus":0.1163775097461439,"score_gpt":0.39908807090467285,"score_spread":0.28271056115852894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403221414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021955578,0.00058904075,0.97607225,0.0006714331,0.00016719838,0.00024915554,0.00027702667,0.0000065312024,0.000011807737],"genre_scores_gemma":[0.8086119,0.000031996857,0.19123977,0.00003879626,0.000049767223,0.0000071871955,0.0000151268705,0.0000039272077,0.0000014696537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985516,0.00009340898,0.0006058805,0.00020757242,0.00044496858,0.00009653379],"domain_scores_gemma":[0.99058104,0.008280165,0.00029579859,0.00010031119,0.0007015658,0.00004114809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010202946,0.00010637513,0.00017320272,0.000081354396,0.00010924959,0.00022343974,0.00029195452,0.00003622557,5.177239e-7],"category_scores_gemma":[0.0015321079,0.000060284907,0.000017300052,0.0001331958,0.00015395778,0.00017706648,0.00010270229,0.00017303268,1.2047192e-7],"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":[0.000074680596,0.00017319218,0.004780001,0.00020948643,0.000023862754,0.0000072985767,0.001635673,0.15271093,0.00011067291,0.42913753,0.00016545149,0.41097122],"study_design_scores_gemma":[0.00015238166,0.000115351635,0.009179472,0.00013675056,0.000013171384,0.000016824702,0.00003415479,0.61675954,0.0000060304747,0.37349722,0.000048589834,0.000040511157],"about_ca_topic_score_codex":0.000025388603,"about_ca_topic_score_gemma":0.000007898649,"teacher_disagreement_score":0.7866564,"about_ca_system_score_codex":0.00003350497,"about_ca_system_score_gemma":0.00007221821,"threshold_uncertainty_score":0.24583471},"labels":[],"label_agreement":null},{"id":"W4403467628","doi":"10.56367/oag-044-11366","title":"Modelling biodiversity is an essential part of its protection","year":2024,"lang":"en","type":"article","venue":"Open Access Government","topic":"Statistical and Computational Modeling","field":"Computer Science","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":"Université de Sherbrooke","funders":"","keywords":"Biodiversity; Environmental science; Environmental resource management; Geography; Ecology; Biology","score_opus":0.1288263573886213,"score_gpt":0.34321665593315226,"score_spread":0.21439029854453096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403467628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021072822,0.00004952677,0.9763879,0.00070600334,0.0003026161,0.00023654687,0.000051364626,0.000032862703,0.0011603502],"genre_scores_gemma":[0.9829705,0.000020135307,0.01663588,0.00014587073,0.000044491248,0.000019415187,0.000002855091,0.0000030338754,0.00015782712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884266,0.000026113816,0.00015490068,0.00033936775,0.0005330654,0.00010388364],"domain_scores_gemma":[0.9996618,0.000025463263,0.00004773676,0.00016202468,0.00004796906,0.000055013636],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00015104661,0.00007242762,0.00008815969,0.000018181436,0.000092791255,0.0011156118,0.0012336811,0.000021957148,0.00012551871],"category_scores_gemma":[0.0000048786155,0.00006597084,0.00002864777,0.00018250373,0.000011440039,0.0021521866,0.0011195067,0.00006171773,0.000045223092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008578899,0.0004226758,0.00011738262,0.000286696,0.00012744676,0.000042281878,0.0013530201,0.49092478,0.00079016737,0.335677,0.0032642512,0.16690852],"study_design_scores_gemma":[0.000082182174,0.000041462845,0.000047697147,0.00004777588,0.000007997396,0.0000014042404,0.0000139946205,0.9826544,0.0056157038,0.009805897,0.0015942265,0.00008728338],"about_ca_topic_score_codex":0.00022307373,"about_ca_topic_score_gemma":0.000002385113,"teacher_disagreement_score":0.9618977,"about_ca_system_score_codex":0.00007506715,"about_ca_system_score_gemma":0.000049961367,"threshold_uncertainty_score":0.9999213},"labels":[],"label_agreement":null},{"id":"W4406397839","doi":"10.1016/0967-0653(95)92026-8","title":"10.1016/0967-0653(95)92026-8","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Statistical and Computational Modeling","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":"Environmental science","score_opus":0.010087940644853879,"score_gpt":0.18824797918783623,"score_spread":0.17816003854298235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406397839","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000033063727,0.000043471522,0.063022695,0.0006050578,0.0000030676981,0.00007680017,0.0000061932114,0.00018405545,0.9360256],"genre_scores_gemma":[0.00023593204,8.1879456e-8,0.026870806,0.00017302457,0.00008835556,0.000010593413,0.000005681966,0.0000075981507,0.9726079],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99905163,0.000027972588,0.00015789882,0.00028859472,0.00024916168,0.00022472513],"domain_scores_gemma":[0.9994174,0.0001221704,0.000015921274,0.00023541332,0.00005860242,0.00015043934],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008493626,0.00010204792,0.00010714091,0.00003652973,0.000087754226,0.00010962057,0.00050412206,0.000028232376,0.9694521],"category_scores_gemma":[0.00002526516,0.00009599294,0.000038215458,0.00026030286,0.000016035116,0.00015335299,0.00009438966,0.000072814044,0.9939783],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008733332,0.00002571079,1.0990741e-8,0.0000018530138,0.0000051813486,0.0000071795002,0.000015353997,0.0045925737,0.0000026695875,0.00072595966,0.05689142,0.93772334],"study_design_scores_gemma":[0.00007601173,0.00005652609,0.000009979024,0.000006768593,0.0000022701668,0.0000071786226,1.2438134e-7,0.26310584,0.000007960221,0.00219582,0.73442227,0.00010926854],"about_ca_topic_score_codex":0.000011812412,"about_ca_topic_score_gemma":3.5790624e-8,"teacher_disagreement_score":0.9376141,"about_ca_system_score_codex":0.000018777817,"about_ca_system_score_gemma":0.000031645715,"threshold_uncertainty_score":0.39144784},"labels":[],"label_agreement":null},{"id":"W4406693501","doi":"10.3390/jrfm18020048","title":"Modeling and Forecasting the Probability of Crypto-Exchange Closures: A Forecast Combination Approach","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Statistical and Computational Modeling","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":"Econometrics; Computer science; Statistics; Mathematics","score_opus":0.024073336744926864,"score_gpt":0.22914842166787316,"score_spread":0.2050750849229463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406693501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12942165,0.0005429904,0.86930746,0.00014481589,0.00009081652,0.00012805768,0.0000015224475,0.000003799628,0.00035886886],"genre_scores_gemma":[0.8723871,0.000195027,0.12733124,0.000048863385,0.000025232881,0.000004444359,2.6733127e-7,0.0000014971284,0.0000063488515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917287,0.000054034394,0.0003633572,0.00012322966,0.00019462183,0.000091879745],"domain_scores_gemma":[0.9994057,0.00014808355,0.00016807744,0.00008309423,0.0001707814,0.000024253713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008009416,0.00006866697,0.00015133977,0.00010536621,0.00014684151,0.000055187,0.00018458671,0.000022443002,2.2024085e-7],"category_scores_gemma":[0.00013859368,0.00004632528,0.00004115015,0.00021017865,0.00003863321,0.00013616939,0.00018341806,0.000110862595,4.1509352e-8],"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":[0.000029758987,0.000058961832,0.00076964224,0.00013634404,0.0000121256735,0.0000015579353,0.00048060218,0.017051226,6.8886516e-7,0.4381668,0.00001795273,0.54327434],"study_design_scores_gemma":[0.0003150871,0.000050127466,0.0069409297,0.00005521565,0.000021161248,0.0000032828923,0.000060876915,0.6422318,0.0000014483574,0.35020334,0.00008345409,0.000033248634],"about_ca_topic_score_codex":0.000018587103,"about_ca_topic_score_gemma":0.0000039570737,"teacher_disagreement_score":0.74296546,"about_ca_system_score_codex":0.000015208092,"about_ca_system_score_gemma":0.000022720507,"threshold_uncertainty_score":0.18890901},"labels":[],"label_agreement":null},{"id":"W4408545256","doi":"10.1061/jhyeff.heeng-6499","title":"Discussion of “Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework”","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Statistical and Computational Modeling","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; Université Laval","funders":"","keywords":"Streamflow; Multivariate statistics; Stage (stratigraphy); Decomposition; Computer science; Artificial intelligence; Multivariate analysis; Machine learning; Hydrology (agriculture); Econometrics; Mathematics; Geology; Geography; Geotechnical engineering; Cartography","score_opus":0.025315935530307723,"score_gpt":0.2749500318004074,"score_spread":0.24963409627009966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408545256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06477462,0.00015101884,0.9342652,0.00027444723,0.00037622583,0.00008065071,0.000005688295,0.000034443405,0.000037714326],"genre_scores_gemma":[0.59096545,0.0000053051667,0.4089597,0.000017701368,0.000034725712,0.0000028467614,0.0000013270471,0.0000037383038,0.0000092136825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989674,0.00003270838,0.0005162617,0.00015204873,0.00016171341,0.00016985195],"domain_scores_gemma":[0.99880016,0.0005984864,0.00027980426,0.00008666884,0.00017976237,0.000055100405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003427335,0.00012195081,0.00026197376,0.00023328829,0.00008163418,0.00004162071,0.00026382247,0.00003702002,0.0000028935792],"category_scores_gemma":[0.00030818218,0.00008481932,0.00012827742,0.00017739384,0.000014051608,0.00032028146,0.000079389836,0.00028936056,1.7075145e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003451492,0.000028734408,0.00008636538,0.00004725412,0.0000410193,0.000009349447,0.000052001098,0.93629295,0.0012411668,0.04428739,0.0000015853134,0.01787765],"study_design_scores_gemma":[0.00045750462,0.00015197363,0.00003082741,0.00023948109,0.000018148618,0.00009612501,0.000006938619,0.8944385,0.0010808432,0.10338591,0.000014930285,0.000078819045],"about_ca_topic_score_codex":0.000004633136,"about_ca_topic_score_gemma":2.0598719e-7,"teacher_disagreement_score":0.5261908,"about_ca_system_score_codex":0.00004266008,"about_ca_system_score_gemma":0.00004051175,"threshold_uncertainty_score":0.34588316},"labels":[],"label_agreement":null},{"id":"W4409069581","doi":"10.18372/2225-5036.30.19208","title":"НЕВИЗНАЧЕНІСТЬ ОЦІНЮВАННЯ КІЛЬКІСНИХ ХАРАКТЕРИСТИК ЯКОСТІ ПРОГРАМНОГО ЗАБЕЗПЕЧЕН-НЯ","year":2024,"lang":"uk","type":"article","venue":"Ukrainian Scientific Journal of Information Security","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"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":"Mathematics","score_opus":0.01680356450854495,"score_gpt":0.2731229693790235,"score_spread":0.25631940487047855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409069581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047487337,0.0037958454,0.9026551,0.007348257,0.027058426,0.0004715471,0.00042860617,0.00022997457,0.010524897],"genre_scores_gemma":[0.9743623,0.0001055584,0.023107175,0.00089665345,0.0009821604,0.0000054172992,0.000059240552,0.000025472029,0.00045601593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99180144,0.00031610267,0.0031894238,0.0006093008,0.0031422854,0.0009414696],"domain_scores_gemma":[0.9935413,0.0009432954,0.0011924832,0.00073488045,0.0027678672,0.0008201663],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005877229,0.00055010663,0.00070626225,0.0018105614,0.00085801276,0.0090159485,0.0022182625,0.00028679235,0.0006231109],"category_scores_gemma":[0.0009998229,0.00048366305,0.0006381005,0.0029668147,0.0006122908,0.009643787,0.00052304193,0.0014024157,0.0022490085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013092537,0.00030760912,0.0000372528,0.0010689686,0.0003933674,0.0003066449,0.21513575,0.0058386633,0.00009243603,0.44632918,0.03827389,0.29208532],"study_design_scores_gemma":[0.0006887144,0.00026832242,0.00028642282,0.00094183604,0.000090021924,0.00074487604,0.001101669,0.619703,0.00012206494,0.064359985,0.31112757,0.0005655122],"about_ca_topic_score_codex":0.000028447097,"about_ca_topic_score_gemma":0.000009183357,"teacher_disagreement_score":0.926875,"about_ca_system_score_codex":0.0003622142,"about_ca_system_score_gemma":0.002843791,"threshold_uncertainty_score":0.9997615},"labels":[],"label_agreement":null},{"id":"W4412229146","doi":"","title":"VUDP-Slutrapport: Renseteknologier for nye pesticidrester (DMS og metabolitter fra alachlor og dimethachlor) på vandværker","year":2023,"lang":"da","type":"report","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Statistical and Computational Modeling","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":"Kruger (Canada)","funders":"","keywords":"Alachlor; Environmental science; Biology; Ecology","score_opus":0.08995835697418544,"score_gpt":0.29108056145131705,"score_spread":0.2011222044771316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412229146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010811029,0.0012112698,0.96521056,0.0031215157,0.0016459306,0.0032275233,0.0022259003,0.0010121695,0.011534128],"genre_scores_gemma":[0.5520777,0.006545959,0.38666967,0.00059600547,0.0007910693,0.0000130552935,0.0010608947,0.00048032566,0.051765297],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9865345,0.0007658509,0.002448416,0.0040789363,0.0036920684,0.0024802273],"domain_scores_gemma":[0.98292583,0.006439326,0.0027488212,0.0031622846,0.0033486586,0.001375084],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0031086043,0.0019570945,0.0044394005,0.0018505718,0.0012005935,0.00016067433,0.007566608,0.0035812387,0.0017319976],"category_scores_gemma":[0.0021477772,0.0023151888,0.0030897632,0.003093507,0.0038284205,0.0011285986,0.0060629137,0.0034085885,0.00078280823],"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":[0.022715922,0.015482047,0.009161648,0.012481945,0.014820598,0.021794511,0.0027907144,0.008365355,0.012805093,0.15060969,0.5373308,0.19164169],"study_design_scores_gemma":[0.030574977,0.011021529,0.157329,0.011453585,0.021551484,0.002483316,0.0039346074,0.10341189,0.0014751103,0.120720215,0.5160079,0.020036384],"about_ca_topic_score_codex":0.0011639235,"about_ca_topic_score_gemma":0.00054448505,"teacher_disagreement_score":0.57854086,"about_ca_system_score_codex":0.0011455673,"about_ca_system_score_gemma":0.002465119,"threshold_uncertainty_score":0.9999952},"labels":[],"label_agreement":null},{"id":"W4415808588","doi":"10.15587/1729-4061.2025.341457","title":"Design of a decision support system for making informed decisions about selection of machines for manufacturing leather garments","year":2025,"lang":"en","type":"article","venue":"Eastern-European Journal of Enterprise Technologies","topic":"Statistical and Computational Modeling","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":"Innovation Cluster (Canada)","funders":"","keywords":"Clothing; Decision support system; Table (database); Process (computing); Selection (genetic algorithm); Task (project management); Production line; Consistency (knowledge bases); Production (economics)","score_opus":0.03316051135140951,"score_gpt":0.3022437081600435,"score_spread":0.26908319680863396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415808588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04420792,0.00010931759,0.95479566,0.00006296845,0.00031063106,0.00029063126,0.00000713411,0.000082110266,0.0001336377],"genre_scores_gemma":[0.6179417,0.000009572526,0.3819956,0.000012742582,0.000009211027,0.000005444473,3.8154195e-7,0.0000068101135,0.000018594905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984188,0.000054634693,0.00092678313,0.00017146597,0.00025167566,0.00017664375],"domain_scores_gemma":[0.9978351,0.0009198148,0.0006698042,0.00018475782,0.0003691963,0.000021340313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062160863,0.00014209774,0.0003185162,0.0005230082,0.00008004842,0.00005683754,0.000873917,0.00003710799,0.0000011351376],"category_scores_gemma":[0.0005425975,0.00010850831,0.00014767304,0.00017244017,0.00004240273,0.00022068655,0.00024812508,0.00010392308,0.0000010632026],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008280613,0.00011395109,0.0006398801,0.00026749843,0.00016526876,0.000018622488,0.00030336625,0.031991187,0.002095751,0.0066282093,0.00018513242,0.9567631],"study_design_scores_gemma":[0.0037615763,0.0023061936,0.002513752,0.006495317,0.0001332545,0.00017525088,0.00081063155,0.8927947,0.04906004,0.041220605,0.0003980426,0.00033059102],"about_ca_topic_score_codex":4.836685e-7,"about_ca_topic_score_gemma":2.7666712e-7,"teacher_disagreement_score":0.95643246,"about_ca_system_score_codex":0.000056201618,"about_ca_system_score_gemma":0.00007879573,"threshold_uncertainty_score":0.44248405},"labels":[],"label_agreement":null},{"id":"W642029847","doi":"10.71781/4481","title":"Optimisation de l'utilisation des techniques de modélisation dans le passage de l'étape pré-clinique à clinique du développement d'un médicament","year":2008,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Statistical and Computational Modeling","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":"Mod; Humanities; Mathematics; Philosophy; Combinatorics","score_opus":0.012643456265625885,"score_gpt":0.21271722157653836,"score_spread":0.20007376531091248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W642029847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25196087,0.00056648994,0.7413017,0.0005857512,0.0002246157,0.0004508315,0.000018403409,0.00033956204,0.0045517595],"genre_scores_gemma":[0.7240654,0.000944661,0.2723563,0.00027879162,0.00018946452,0.00017599273,0.00048489537,0.000035407626,0.0014690799],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967796,0.0004167927,0.00068972213,0.00078160537,0.0007720124,0.0005602397],"domain_scores_gemma":[0.9979395,0.00028234883,0.0005429572,0.0003916181,0.00048101298,0.0003625104],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007111953,0.0004537525,0.0003690932,0.0003402077,0.0044576265,0.0001541785,0.00073525705,0.00050377107,0.0000111680465],"category_scores_gemma":[0.00016387929,0.0005429586,0.00025221927,0.00034375273,0.00019498616,0.00063862803,0.00016946062,0.00046679124,0.000009434317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011313217,0.001668301,0.027372643,0.00051292864,0.00068520167,0.0025217608,0.14800058,0.1369958,0.03165544,0.4799353,0.001165763,0.16835496],"study_design_scores_gemma":[0.001430226,0.00036193765,0.14778422,0.0006039744,0.00023835008,0.0012900064,0.0074647185,0.6309398,0.08907251,0.11763222,0.0017669298,0.0014151228],"about_ca_topic_score_codex":0.008076832,"about_ca_topic_score_gemma":0.00089974765,"teacher_disagreement_score":0.49394396,"about_ca_system_score_codex":0.008348243,"about_ca_system_score_gemma":0.004750536,"threshold_uncertainty_score":0.9997022},"labels":[],"label_agreement":null},{"id":"W6925141876","doi":"10.15493/dea.mims.15772023","title":"Long-term observations of hourly bottom temperatures on the Prince Edward Island shelf at M2 (April 2024 - April 2025)","year":2024,"lang":"en","type":"dataset","venue":"SAEON Data Centre","topic":"Statistical and Computational Modeling","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":"Circumpolar star; Current (fluid); Thermohaline circulation; Ocean current; Indian ocean; North Atlantic Deep Water; Circulation (fluid dynamics)","score_opus":0.04355299054731677,"score_gpt":0.28489295823533506,"score_spread":0.2413399676880183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925141876","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039225916,0.0010398772,0.007740638,0.0046195644,0.001932478,0.0003642851,0.9837949,0.00006736056,0.000048621696],"genre_scores_gemma":[0.00061296485,0.00047884625,0.003998853,0.0010724133,0.0004724888,0.000016304612,0.9918289,0.000025433736,0.0014938067],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99667484,0.00014739043,0.0005803586,0.001196017,0.000979101,0.00042226823],"domain_scores_gemma":[0.99572366,0.00092898775,0.0002203879,0.002822874,0.00016465347,0.00013940428],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030628766,0.00043890564,0.00040462604,0.00013543664,0.0002500566,0.00048989645,0.004333669,0.00019025391,0.0004189178],"category_scores_gemma":[0.00027670682,0.00030129077,0.0001122946,0.0005264477,0.0001219224,0.00031590322,0.0034931737,0.0008259453,0.0013433758],"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":[0.000020491245,0.000084926665,0.00020659422,0.00024169649,0.00011473286,0.0000911303,0.00004733292,0.00013735532,0.0000062918903,0.0051872795,0.99314624,0.00071594614],"study_design_scores_gemma":[0.00025399402,0.0000653121,0.004821445,0.0010675249,0.00017725617,0.000023584322,0.00001414864,0.0229334,0.000015883572,0.004235918,0.96579665,0.000594859],"about_ca_topic_score_codex":0.00010681543,"about_ca_topic_score_gemma":0.0006001308,"teacher_disagreement_score":0.027349547,"about_ca_system_score_codex":0.00009491655,"about_ca_system_score_gemma":0.00035362516,"threshold_uncertainty_score":0.9999439},"labels":[],"label_agreement":null},{"id":"W6930442310","doi":"10.5281/zenodo.12503163","title":"casino rama shuttle bus schedule pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical and Computational Modeling","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":"Ticket; Schedule; Service (business); Club; Entertainment; Table (database); Backup","score_opus":0.032680289410193526,"score_gpt":0.2531995115702068,"score_spread":0.2205192221600133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930442310","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031134336,0.00030064397,0.40492442,0.0004683957,0.0002528598,0.00015009442,0.00010090905,0.0011720242,0.5926275],"genre_scores_gemma":[0.0059571373,0.00010190092,0.044863295,0.00052639365,0.0010080333,1.3541624e-7,0.0013545334,0.009050606,0.93713796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981775,0.00014316938,0.00021264881,0.00065355306,0.00047985654,0.00033323688],"domain_scores_gemma":[0.99897677,0.00002589134,0.000081122365,0.000503578,0.00023732225,0.00017533799],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00024784185,0.00019877263,0.0001777091,0.00033953117,0.0004968093,0.0015078554,0.001612076,0.000114428105,0.03733949],"category_scores_gemma":[0.00020310073,0.00020073849,0.00006340793,0.0005550255,0.00009287529,0.00011838068,0.0017154331,0.00034217676,0.20802774],"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":[0.0000029481964,0.000037930426,2.7559231e-8,0.00008537155,0.000037554084,0.000044200493,0.00011166309,0.000049157192,0.000026764075,0.1259615,0.8197558,0.05388709],"study_design_scores_gemma":[0.00014752438,0.000068963345,0.0000040218106,0.00011684539,0.000011853879,0.00010226672,0.000016006796,0.01849289,0.000013518815,0.008028975,0.9727717,0.00022541957],"about_ca_topic_score_codex":0.00002155387,"about_ca_topic_score_gemma":2.4701436e-7,"teacher_disagreement_score":0.36006114,"about_ca_system_score_codex":0.000087763554,"about_ca_system_score_gemma":0.000008324027,"threshold_uncertainty_score":0.99952865},"labels":[],"label_agreement":null},{"id":"W6995942656","doi":"","title":"Prospectus of McGill College, Montreal; founded by bequest of the Hon. James McGill, in 1811, erected into a university by Royal Charter in 1821, and re-organized by an amended charter in 1852. Session of 1856-7.","year":2014,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Statistical and Computational Modeling","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":"Prospectus; Charter; Bequest; Foundation (evidence); Work (physics)","score_opus":0.004189328581393039,"score_gpt":0.1720272751837829,"score_spread":0.16783794660238988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995942656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9830686,0.000014410965,0.007711518,0.0075857015,0.000048667047,0.00043269634,0.0004280833,0.00006031639,0.00065004826],"genre_scores_gemma":[0.994067,0.00002624596,0.004760137,0.00010972734,0.000003724904,5.143445e-7,0.000038723967,0.000011356396,0.0009825852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9983501,0.00040367333,0.00026237222,0.00047006548,0.0002722682,0.00024153198],"domain_scores_gemma":[0.9990523,0.00024140718,0.00022021658,0.00029357165,0.00008040733,0.00011212826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009565834,0.00019305688,0.00040917489,0.0002501617,0.00010473221,0.00001513175,0.00060122705,0.00012898803,0.000017534056],"category_scores_gemma":[0.000031856125,0.00018641246,0.00004607916,0.0009373056,0.00016549132,0.0008772992,0.00040933175,0.00020969093,8.0103246e-7],"study_design_candidate":"observational","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":[0.008554755,0.011748486,0.24565287,0.0017037874,0.0005443391,0.00043608216,0.02650853,0.0043509565,0.034291483,0.32557902,0.31870532,0.021924382],"study_design_scores_gemma":[0.045940023,0.0047435295,0.35620573,0.0025622926,0.000206345,0.000004671831,0.009341037,0.35618362,0.11816134,0.0721994,0.030206954,0.004245058],"about_ca_topic_score_codex":0.01128659,"about_ca_topic_score_gemma":0.0006450396,"teacher_disagreement_score":0.35183266,"about_ca_system_score_codex":0.00010849036,"about_ca_system_score_gemma":0.000073160285,"threshold_uncertainty_score":0.9952973},"labels":[],"label_agreement":null},{"id":"W7000840501","doi":"","title":"Halifax OK's $40,000 solo trash study","year":2022,"lang":"en","type":"other","venue":"Saint Mary's University Institutional Repository (Saint Mary's University)","topic":"Statistical and Computational Modeling","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":"","score_opus":0.01124655043078395,"score_gpt":0.1932556493601557,"score_spread":0.18200909892937175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000840501","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028378202,0.00016526043,0.29544443,0.0006121669,0.0025677036,0.0012858267,0.00056095206,0.0013768484,0.695149],"genre_scores_gemma":[0.11098642,0.000116076044,0.020595433,0.00041534356,0.0007272583,0.0000048979605,0.00064181176,0.00023376159,0.866279],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99453354,0.000782376,0.00045122008,0.0018399229,0.0016198824,0.0007730317],"domain_scores_gemma":[0.9972044,0.00034493196,0.00048883824,0.0011255643,0.00028748024,0.0005487802],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032657236,0.0008089925,0.00079922733,0.0021033776,0.002083637,0.0001935969,0.0032729243,0.00039378833,0.0017305563],"category_scores_gemma":[0.000053234235,0.0010118168,0.00046987616,0.0016914955,0.0005683338,0.0006818346,0.0026298945,0.0010791036,0.00018325204],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004935705,0.0023742772,0.002354114,0.00014812607,0.0011860927,0.028498124,0.00049997243,0.013257529,0.000021406735,0.8567049,0.09003866,0.0044232677],"study_design_scores_gemma":[0.002098054,0.00054753287,0.0025933222,0.00012919173,0.00029948616,0.00026571337,0.0010052779,0.008255258,0.0000036083843,0.0011291578,0.9822898,0.001383633],"about_ca_topic_score_codex":0.0073852395,"about_ca_topic_score_gemma":0.00039057713,"teacher_disagreement_score":0.89225113,"about_ca_system_score_codex":0.0047251675,"about_ca_system_score_gemma":0.001452257,"threshold_uncertainty_score":0.99923325},"labels":[],"label_agreement":null},{"id":"W7001440376","doi":"","title":"Kenneth Milton Chapman: A Life Dedicated to Indian Arts and Artists. By Janet Chapman and Karen Barrie.","year":2010,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Statistical and Computational Modeling","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":"The arts; Performance art","score_opus":0.00937462055494304,"score_gpt":0.20610123615888953,"score_spread":0.1967266156039465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001440376","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9333073,0.0003271886,0.05622197,0.0059409705,0.00020025896,0.0003628938,0.0013251699,0.00043411698,0.0018801261],"genre_scores_gemma":[0.978639,0.00001002556,0.01711131,0.0036948682,0.000109854336,0.000012382679,0.00026128403,0.000034572917,0.00012667387],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99792004,0.00003758375,0.00039324033,0.0007520002,0.00040104642,0.0004961066],"domain_scores_gemma":[0.9980167,0.00023037216,0.00008441708,0.00033772032,0.00004414544,0.0012866101],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00015336338,0.00029814919,0.00025906364,0.0001747792,0.00021951739,0.0019500643,0.00061012356,0.0001428629,0.00007079889],"category_scores_gemma":[0.00034935807,0.00027562407,0.00004049319,0.00041390327,0.00013037169,0.0023541218,0.0006230629,0.0004822797,0.00037394548],"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":[0.0003508017,0.0008752075,0.09153647,0.00032954031,0.00022838118,0.00045529194,0.0015016951,0.00011352415,0.003551011,0.39834082,0.049704805,0.45301247],"study_design_scores_gemma":[0.0030851443,0.0009468715,0.07946464,0.00029256486,0.0000429139,0.0003800816,0.00006149827,0.11428475,0.003678037,0.3744372,0.41992754,0.00339875],"about_ca_topic_score_codex":0.0000074783243,"about_ca_topic_score_gemma":0.0000029677676,"teacher_disagreement_score":0.44961372,"about_ca_system_score_codex":0.0000076598935,"about_ca_system_score_gemma":0.000086676824,"threshold_uncertainty_score":0.9999696},"labels":[],"label_agreement":null},{"id":"W7001739378","doi":"","title":"Les 101 disques qui ont marqué le Québec","year":2008,"lang":"fr","type":"article","venue":"","topic":"Statistical and Computational Modeling","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":"","score_opus":0.07603983343880447,"score_gpt":0.2751876759028064,"score_spread":0.19914784246400194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001739378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01229696,0.0036475742,0.9345772,0.021039005,0.00057826354,0.000078549725,0.000007002816,0.00013275765,0.027642688],"genre_scores_gemma":[0.78129923,0.000057655772,0.18852988,0.000907892,0.00025343517,0.000008515237,0.0000032937407,0.000011132631,0.028928945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985278,0.00005987141,0.00030749952,0.00041272506,0.0003407083,0.00035137212],"domain_scores_gemma":[0.9989761,0.00037888542,0.0000488091,0.000233429,0.0001809117,0.00018183193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009018287,0.00017770092,0.00018835535,0.000042916465,0.00040533024,0.00011151596,0.0004324849,0.00006988832,0.00049858005],"category_scores_gemma":[0.00006495603,0.00016335044,0.00009403446,0.00018307407,0.00023226837,0.00035441158,0.00023196572,0.00015298024,0.0005660187],"study_design_candidate":"simulation_or_modeling","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":[0.000005274704,0.00014619347,0.0011887876,0.00001659525,0.000016990685,0.00008403242,0.0005896697,0.0035036334,0.0000067501387,0.78805715,0.0052753086,0.2011096],"study_design_scores_gemma":[0.00021705795,0.00009470747,0.018802198,0.00004223452,0.000007555466,0.00011474758,0.000047038917,0.8687814,0.000059023318,0.08225221,0.02930406,0.0002777749],"about_ca_topic_score_codex":0.3372726,"about_ca_topic_score_gemma":0.029729836,"teacher_disagreement_score":0.86527777,"about_ca_system_score_codex":0.0000940467,"about_ca_system_score_gemma":0.0007296276,"threshold_uncertainty_score":0.98797506},"labels":[],"label_agreement":null},{"id":"W7001845639","doi":"","title":"L’engagement des bénéficiaires dans les projets de développement international : les perspectives des superviseurs, des coordonnateurs et des bénéficiaires","year":2023,"lang":"fr","type":"other","venue":"Constellation (Université du Québec à Chicoutimi)","topic":"Statistical and Computational Modeling","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":"Management by objectives; Work (physics); Context (archaeology)","score_opus":0.057816532083822904,"score_gpt":0.2710593687335409,"score_spread":0.21324283664971802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001845639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13991052,0.0037191254,0.7319387,0.0038160258,0.0004785644,0.00059687014,0.00012320676,0.00071927486,0.118697695],"genre_scores_gemma":[0.92821854,0.0037431233,0.055670973,0.0001398015,0.00026537373,0.000016996182,0.00006568035,0.00024959102,0.011629933],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99590963,0.0006459109,0.00062372664,0.0011915137,0.0008245175,0.00080469565],"domain_scores_gemma":[0.9964401,0.0013364028,0.00038125858,0.00038015615,0.0011252386,0.00033684055],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.00070173235,0.0007629184,0.0005403691,0.0006851753,0.0022656952,0.0005686897,0.001234574,0.00030813733,0.009186151],"category_scores_gemma":[0.000489634,0.0008354749,0.0003099399,0.00075896,0.0030510998,0.00074943766,0.00080537284,0.0005402162,0.00011633821],"study_design_candidate":"observational","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":[0.000103822415,0.0004912955,0.12720208,0.00035170524,0.000799718,0.00020639978,0.16356716,0.06395632,0.00014974069,0.21049795,0.0024882855,0.43018553],"study_design_scores_gemma":[0.0011440171,0.00024367138,0.5378691,0.0018131472,0.00029887995,0.00012252129,0.03549569,0.38955086,0.000083885294,0.025144136,0.006968144,0.0012659675],"about_ca_topic_score_codex":0.026605777,"about_ca_topic_score_gemma":0.08959714,"teacher_disagreement_score":0.788308,"about_ca_system_score_codex":0.0028308737,"about_ca_system_score_gemma":0.0017155339,"threshold_uncertainty_score":0.99966204},"labels":[],"label_agreement":null},{"id":"W7002029368","doi":"","title":"Meşrutiyet ve Mütareke Dönemleri Cemiyet Yapılanmalarında Babanlar/Babanzadeler","year":2023,"lang":"en","type":"article","venue":"DergiPark (Istanbul University)","topic":"Statistical and Computational Modeling","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":"Solidarity; Ottoman empire; Constitutional court; Quarter (Canadian coin); Period (music); Restructuring; Ideology","score_opus":0.01875434219465596,"score_gpt":0.20538974106660504,"score_spread":0.18663539887194908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002029368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04340727,0.00005493489,0.9254589,0.0018446656,0.0006278003,0.00018758104,0.000065739965,0.0011264685,0.027226623],"genre_scores_gemma":[0.96300983,0.00006102021,0.028828088,0.00056202424,0.00012272509,0.0000029285175,0.00009261451,0.000030864394,0.007289909],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781686,0.00010768074,0.00024688893,0.0006993474,0.0005371301,0.00059209066],"domain_scores_gemma":[0.9984788,0.00043207617,0.00009309989,0.0005150967,0.00021361052,0.00026734528],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019518766,0.00025328735,0.0002609635,0.00048327577,0.000386181,0.00016066284,0.0011993063,0.00011096776,0.00012483032],"category_scores_gemma":[0.0000767565,0.00028305742,0.00014198001,0.002119247,0.00009088071,0.00055613444,0.00056798296,0.0002296334,0.0007773105],"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":[0.00005833947,0.000079862664,0.0003600637,0.00003726873,0.000099152996,0.00154838,0.001039111,0.011622379,0.00019856183,0.951868,0.026720673,0.0063682063],"study_design_scores_gemma":[0.0015407006,0.00016280745,0.005490782,0.00007699065,0.000054219276,0.00006490367,0.0008358384,0.6243427,0.00025568443,0.07036629,0.29567054,0.0011386005],"about_ca_topic_score_codex":0.00004863196,"about_ca_topic_score_gemma":0.000017599448,"teacher_disagreement_score":0.9196026,"about_ca_system_score_codex":0.00014800973,"about_ca_system_score_gemma":0.00018477804,"threshold_uncertainty_score":0.99996215},"labels":[],"label_agreement":null},{"id":"W7002092896","doi":"","title":"Methods for the Translocation of the Yellow Lampmussel (&lt;em&gt;Lampsilis cariosa&lt;/em&gt;) and the Tidewater Mucket (&lt;em&gt;Leptodea ochracea&lt;/em&gt;) in the Fort Halifax Dam Impoundment of the Sebasticok River, Maine","year":2007,"lang":"en","type":"article","venue":"DigitalCommons (California Polytechnic State University)","topic":"Statistical and Computational Modeling","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":"U.S. Geological Survey","keywords":"Tidewater; Habitat; Threatened species; Aerial survey; Mussel; Transponder (aeronautics); Radiata","score_opus":0.01651469089277526,"score_gpt":0.24769827690717552,"score_spread":0.23118358601440026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002092896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08805358,0.00046294663,0.9038399,0.0030141694,0.0002785071,0.0019544926,0.0009994472,0.000101801306,0.0012951834],"genre_scores_gemma":[0.97299075,0.00013363396,0.025803631,0.00049202726,0.00004731446,0.000037591693,0.00006176933,0.000041353287,0.00039192147],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957205,0.00059285364,0.0010360547,0.00076102855,0.0010395062,0.0008500569],"domain_scores_gemma":[0.9929936,0.0043016374,0.0005687407,0.0015472395,0.0004339568,0.00015481339],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002787309,0.00056242,0.00065357913,0.000404108,0.00081711204,0.0002880759,0.0032574148,0.0001841287,0.000009168792],"category_scores_gemma":[0.00031808883,0.00030364338,0.00054819905,0.0020254652,0.0010692277,0.000470535,0.0009625154,0.0005030284,0.0000047583485],"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":[0.0016272638,0.0013250277,0.0012052386,0.0003757019,0.0010380474,0.000056205037,0.023909518,0.03435874,0.005001177,0.6503742,0.0029992464,0.27772963],"study_design_scores_gemma":[0.010438143,0.0007034579,0.016128125,0.0007185722,0.0011668399,0.00020870913,0.0063143466,0.7201147,0.009241491,0.14229302,0.09057107,0.0021015469],"about_ca_topic_score_codex":0.0002245088,"about_ca_topic_score_gemma":0.002291492,"teacher_disagreement_score":0.88493717,"about_ca_system_score_codex":0.00023190374,"about_ca_system_score_gemma":0.00027957367,"threshold_uncertainty_score":0.9999416},"labels":[],"label_agreement":null},{"id":"W7002331750","doi":"","title":"Mise à jour sur l'ajout d'un objectif sur l'utilisation efficiente de l'eau aux codes modèles de construction","year":2012,"lang":"fr","type":"article","venue":"NPARC","topic":"Statistical and Computational Modeling","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":"Construction industry; Context (archaeology); Work (physics)","score_opus":0.0429119286434048,"score_gpt":0.26785638325702477,"score_spread":0.22494445461361998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002331750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31078604,0.0010537322,0.6832205,0.0017361164,0.001544807,0.00012145561,0.000029365923,0.00008748586,0.0014204732],"genre_scores_gemma":[0.7375837,0.00005352188,0.2608642,0.0003299661,0.0004904601,0.00001327267,0.0000098256205,0.00001664453,0.00063839747],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974169,0.0003544651,0.00043862135,0.00040242085,0.00051187427,0.0008757236],"domain_scores_gemma":[0.9979685,0.0007337484,0.0001654308,0.00026098857,0.00045324827,0.0004181104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082842645,0.00025904865,0.00025704244,0.00011667754,0.0003873408,0.00029287324,0.00036218067,0.00016843421,0.00025473145],"category_scores_gemma":[0.00053585897,0.00027889182,0.00012336187,0.0003667384,0.00023332416,0.0007524531,0.00014379526,0.00032078204,0.00013428552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053580578,0.00035248848,0.010728241,0.0001139375,0.000069083886,0.000018747101,0.0039850557,0.03446527,0.006474597,0.6069984,0.0011846317,0.33555597],"study_design_scores_gemma":[0.00045636602,0.000055562778,0.017210817,0.000114079136,0.000051980198,0.0002912597,0.00015256826,0.86613977,0.0031464975,0.11093711,0.0011657282,0.00027828282],"about_ca_topic_score_codex":0.00047969772,"about_ca_topic_score_gemma":0.00002918892,"teacher_disagreement_score":0.83167446,"about_ca_system_score_codex":0.00028078732,"about_ca_system_score_gemma":0.00066092797,"threshold_uncertainty_score":0.9999663},"labels":[],"label_agreement":null},{"id":"W7002931274","doi":"","title":"Fru 27","year":2015,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Statistical and Computational Modeling","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":"Estate; George (robot)","score_opus":0.009989730900589371,"score_gpt":0.19783349142668435,"score_spread":0.18784376052609497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002931274","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.4651e-7,0.00019945299,0.0011700144,0.00015075516,0.00046582872,0.00015734942,0.000078431374,0.00025003744,0.9975275],"genre_scores_gemma":[0.000085123116,0.000015986694,0.03984782,0.0003379195,0.00012907865,0.000005066229,0.00007087751,0.000045911987,0.9594622],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985768,0.000043267344,0.00043093297,0.000199455,0.00053308334,0.0002164671],"domain_scores_gemma":[0.99890137,0.00010298394,0.00029250095,0.00032837995,0.0002332514,0.00014151745],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015224329,0.00022420073,0.00027918306,0.00006550477,0.000033359593,0.00007791554,0.00062070496,0.00019383368,0.06882366],"category_scores_gemma":[0.00008979354,0.0002126648,0.00007440643,0.000008168846,0.000059007405,1.5980054e-7,0.00018247502,0.00016427558,0.016492773],"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":[0.000007152388,0.000016373202,1.1831748e-7,0.00009455731,0.000022150141,0.000011454553,0.000060798466,0.00069210905,1.693037e-8,0.0027430942,0.9915408,0.0048113707],"study_design_scores_gemma":[0.00023171782,0.00006735777,7.442819e-7,0.00010100587,0.000011741637,0.000036983827,0.000009953059,0.002964839,6.921534e-7,0.0010411789,0.9952967,0.0002370801],"about_ca_topic_score_codex":0.0010624569,"about_ca_topic_score_gemma":0.00013348214,"teacher_disagreement_score":0.05233088,"about_ca_system_score_codex":0.000028676499,"about_ca_system_score_gemma":0.00006717246,"threshold_uncertainty_score":0.984273},"labels":[],"label_agreement":null},{"id":"W7006784886","doi":"","title":"World’s largest wastewater energy transfer project to supply renewable energy to Toronto Western Hospital","year":2021,"lang":"en","type":"other","venue":"","topic":"Statistical and Computational Modeling","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":"Renewable energy; Wastewater; Energy supply; Energy (signal processing); Energy consumption; Water-energy nexus","score_opus":0.013286721975156048,"score_gpt":0.24430591604120497,"score_spread":0.23101919406604893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006784886","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000051655597,0.0003776802,0.74845636,0.00086401985,0.0007390527,0.00014390971,0.000039133392,0.00022640511,0.24914825],"genre_scores_gemma":[0.0030426914,0.000032154,0.12319933,0.0035467276,0.00057266385,0.00018272434,0.000101834536,0.0001953094,0.86912656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972963,0.00006974831,0.00035580707,0.001111185,0.00057425967,0.00059273967],"domain_scores_gemma":[0.9988524,0.0000679312,0.000028607275,0.0006124659,0.000107429456,0.00033118395],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000062978805,0.00044711772,0.00046882077,0.0002462995,0.00006018199,0.00033355659,0.0009789873,0.00014454135,0.0024072668],"category_scores_gemma":[0.000008167177,0.00038230087,0.00012809364,0.00041774623,0.0000136814415,0.0001343844,0.00040458608,0.00007084697,0.00006562123],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015433607,0.00024452494,0.000023434506,0.00005338596,0.00013272528,0.00013849585,0.00042341754,0.002153657,0.00009198824,0.1565209,0.82425725,0.015944773],"study_design_scores_gemma":[0.00024463897,0.00021993101,0.00001926492,0.00026165738,0.0000133111025,0.000008339951,0.000020506492,0.005213602,0.0007118172,0.000992617,0.99147594,0.00081834913],"about_ca_topic_score_codex":0.072826914,"about_ca_topic_score_gemma":0.17713755,"teacher_disagreement_score":0.6252571,"about_ca_system_score_codex":0.000121214376,"about_ca_system_score_gemma":0.000199709,"threshold_uncertainty_score":0.9998629},"labels":[],"label_agreement":null},{"id":"W7010761547","doi":"","title":"It Takes a Village to Reduce Recidivism: Examing Ex-Offenders DEI &amp; Belonging in Higher Education","year":2023,"lang":"en","type":"article","venue":"NSUWorks (Nova Southeastern University)","topic":"Statistical and Computational Modeling","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":"Recidivism; Counterfactual thinking; Criminal justice; Higher education; Prison; Politics; Stigma (botany); Economic Justice","score_opus":0.09865798377099973,"score_gpt":0.28289054893301024,"score_spread":0.18423256516201053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010761547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16859834,0.000023731705,0.8120388,0.005314829,0.001021756,0.00022503707,0.0000064106835,0.00030644124,0.012464646],"genre_scores_gemma":[0.9629796,0.000002521474,0.022089437,0.00057861843,0.00011014453,0.0000019426045,0.000013904578,0.000017573724,0.014206253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835396,0.00009346543,0.00021082246,0.00058589084,0.00033171487,0.00042416193],"domain_scores_gemma":[0.999076,0.00022671376,0.00006640625,0.00034173802,0.00010854033,0.00018059391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021246937,0.00018136077,0.00017947759,0.0008554859,0.00014586304,0.00013008849,0.0007229522,0.00008880446,0.000053260424],"category_scores_gemma":[0.000042321604,0.00021109416,0.00005659205,0.0024219626,0.000028963495,0.00034559242,0.00028560252,0.00020093369,0.00052641693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017647781,0.00037463297,0.0095069455,0.00012476304,0.00010828424,0.00040228473,0.028737618,0.1386773,0.0013085415,0.46437803,0.0022200656,0.35398504],"study_design_scores_gemma":[0.0045428667,0.0003190782,0.18004388,0.003035113,0.0001195776,0.000082666505,0.026029916,0.65209776,0.00019980736,0.050873864,0.07817305,0.004482453],"about_ca_topic_score_codex":0.00009297656,"about_ca_topic_score_gemma":0.00007662147,"teacher_disagreement_score":0.79438126,"about_ca_system_score_codex":0.00011592531,"about_ca_system_score_gemma":0.00014251555,"threshold_uncertainty_score":0.860817},"labels":[],"label_agreement":null},{"id":"W7014244859","doi":"","title":"Oppy Announces Strategic Partnership with DEEP to Build Canada’s Greenest Greenhouse","year":2024,"lang":"en","type":"other","venue":"","topic":"Statistical and Computational Modeling","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":"General partnership; Government (linguistics); Greenhouse; Sustainability","score_opus":0.03139542731746933,"score_gpt":0.2546566561822689,"score_spread":0.22326122886479957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014244859","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006380443,0.0003267538,0.53862906,0.0014769085,0.00022549559,0.00013330988,0.000020508764,0.00031832576,0.4588633],"genre_scores_gemma":[0.0144215925,0.000011064633,0.19468345,0.0033212192,0.00044254868,0.000052957676,0.000025338197,0.00027400194,0.78676784],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99811983,0.000026334192,0.0001853748,0.00068783876,0.00064014056,0.000340463],"domain_scores_gemma":[0.99917597,0.000070975126,0.00005104962,0.0003626447,0.00007002988,0.000269323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004887926,0.00030016774,0.0002502934,0.00014300573,0.000040165047,0.000272575,0.000773401,0.000080671,0.00018466549],"category_scores_gemma":[0.0000059288195,0.00021118647,0.000029938175,0.00036706077,0.00002909913,0.00005124909,0.0001410135,0.00016346855,0.00033016744],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000464888,0.000014612906,0.000015804288,0.000077598546,0.000059193677,0.00030063005,0.000040825118,0.0018816742,3.319345e-7,0.49427167,0.5007946,0.0025384398],"study_design_scores_gemma":[0.00032493164,0.00039983322,0.00013760531,0.00074585533,0.00007153419,0.00011702631,0.00014366343,0.24111746,0.0000045637053,0.09706418,0.65804446,0.0018288994],"about_ca_topic_score_codex":0.34373143,"about_ca_topic_score_gemma":0.9007976,"teacher_disagreement_score":0.55706614,"about_ca_system_score_codex":0.000068760404,"about_ca_system_score_gemma":0.0010663697,"threshold_uncertainty_score":0.8611935},"labels":[],"label_agreement":null},{"id":"W7067710671","doi":"","title":"Meurtre et révolte dans sept pièces de théâtre québécoises pré-Révolution tranquille (1952-1959) (\"De l'autre côté du mur\", \"Zone\" et \"Le Naufragé\" de Marcel Dubé, \"Le Choix des armes\" et \"La Toile d'araignée\" d'Hubert Aquin, \"Un fils à tuer\" d'Éloi de Grandmont et \"La Mercière assassinée\" d'Anne Hébert)","year":2012,"lang":"fr","type":"other","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Statistical and Computational Modeling","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":"Conscience; Cote d ivoire; Collective memory","score_opus":0.011532987551640278,"score_gpt":0.2187158610921124,"score_spread":0.20718287354047213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067710671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054546334,0.0065892315,0.7929197,0.0026971982,0.00034754208,0.00051954726,0.0001903436,0.00014881168,0.14204131],"genre_scores_gemma":[0.85451615,0.0016090636,0.11871156,0.0004908184,0.00016939567,0.0000133277335,0.00010355656,0.00010960039,0.02427652],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99423146,0.0022217638,0.00072157965,0.00096856087,0.00083443255,0.001022178],"domain_scores_gemma":[0.9966683,0.0012951234,0.00044105257,0.000637268,0.0004169286,0.00054133445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014559416,0.0008740424,0.0009690734,0.00035126603,0.0011686224,0.00020004949,0.0020449948,0.0007329088,0.0002531922],"category_scores_gemma":[0.00027139252,0.0008766892,0.0008193416,0.00059466966,0.002020844,0.0010067165,0.0008269109,0.0009719821,0.000034017725],"study_design_candidate":"simulation_or_modeling","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":[0.00048631083,0.0034539474,0.007058703,0.003368557,0.0010487909,0.00020779646,0.028037792,0.30337515,0.01615825,0.61192435,0.008726734,0.01615361],"study_design_scores_gemma":[0.0047056805,0.0003497449,0.22719899,0.010772911,0.0010698405,0.0012372922,0.0008501107,0.6068755,0.0063804905,0.01558719,0.12315634,0.0018159021],"about_ca_topic_score_codex":0.036189012,"about_ca_topic_score_gemma":0.058249455,"teacher_disagreement_score":0.79996985,"about_ca_system_score_codex":0.0013979613,"about_ca_system_score_gemma":0.0036661946,"threshold_uncertainty_score":0.99936837},"labels":[],"label_agreement":null},{"id":"W7070104230","doi":"","title":"Ontario farm groundwater quality survey - Summer 1992","year":2018,"lang":"en","type":"report","venue":"The Atrium (University of Guelph)","topic":"Statistical and Computational Modeling","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":"Ontario Ministry of Food and Agriculture; University of Waterloo; Hellenic Ministry of Environment and Energy","keywords":"Agriculture; Groundwater; Water quality; Farm water; Sampling (signal processing); Hydrology (agriculture); Water well; Work (physics)","score_opus":0.1100396197324537,"score_gpt":0.294379955417893,"score_spread":0.18434033568543934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070104230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033419397,0.0001153494,0.94621426,0.00066443987,0.0011581182,0.00021564793,0.00010775124,0.00007644795,0.018028604],"genre_scores_gemma":[0.9718224,0.00005161697,0.012560761,0.00010127362,0.00018688713,2.4259717e-7,0.00017311229,0.0000129969285,0.015090707],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9974282,0.00033757125,0.00030289643,0.0005287793,0.0010973917,0.00030519688],"domain_scores_gemma":[0.99721706,0.0005833466,0.0003913204,0.0007977049,0.00090689026,0.00010367068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020155562,0.00023455867,0.00046743362,0.000110329536,0.0003483255,0.00006983656,0.0019877271,0.00018530637,0.00026528386],"category_scores_gemma":[0.00009088038,0.00020325604,0.00023655585,0.0002850533,0.0002777432,0.0001807918,0.0009828865,0.00037930426,0.00018175777],"study_design_candidate":"observational","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":[0.001768041,0.0024834871,0.12635337,0.0019101917,0.0054883845,0.0007868944,0.042849407,0.0017685042,0.0012845829,0.19038416,0.43119854,0.19372442],"study_design_scores_gemma":[0.0003608757,0.00014243671,0.917505,0.000057540303,0.00010267986,0.000025017092,0.000109537425,0.004374123,0.000004386636,0.010941278,0.06587378,0.0005033167],"about_ca_topic_score_codex":0.56021774,"about_ca_topic_score_gemma":0.29258046,"teacher_disagreement_score":0.938403,"about_ca_system_score_codex":0.000365928,"about_ca_system_score_gemma":0.0012053464,"threshold_uncertainty_score":0.8288541},"labels":[],"label_agreement":null},{"id":"W7112709870","doi":"","title":"ВИКОРИСТАННЯ АГРЕГОВАНИХ КРИТЕРІЇВ ДЛЯ ОЦІНКИ ЯКОСТІ ТЕСТІВ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ","year":2024,"lang":"en","type":"article","venue":"The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Montmorency","funders":"","keywords":"Software; Software quality; Set (abstract data type); Quality (philosophy); Software metric; Selection (genetic algorithm); Software system","score_opus":0.0765819397975161,"score_gpt":0.34409115452519107,"score_spread":0.26750921472767497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112709870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16995044,0.0038989016,0.7189798,0.06105954,0.008852493,0.0011680808,0.00078622816,0.0017190217,0.033585515],"genre_scores_gemma":[0.91176176,0.000085410335,0.019097982,0.00027347772,0.00046549845,0.000005160842,0.00011125016,0.00002511292,0.068174325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953223,0.00028223477,0.0005698392,0.0011241699,0.0021235181,0.00057796325],"domain_scores_gemma":[0.99654114,0.0010279652,0.00017943031,0.0006306401,0.0014062464,0.00021460072],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001207902,0.0003746148,0.00041777035,0.00057277875,0.00092877785,0.00075702294,0.0027185879,0.00016631823,0.00048800392],"category_scores_gemma":[0.0004503074,0.00029610566,0.00029323672,0.0016122857,0.0011919017,0.0015663372,0.0011188207,0.00044921067,0.00041395682],"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":[0.00006651329,0.00013790705,0.000052347597,0.00008346507,0.000102686725,0.00008291257,0.0027956679,0.0023661633,0.00059388933,0.97755265,0.008943598,0.007222188],"study_design_scores_gemma":[0.0006754213,0.00039362986,0.002085315,0.00016404939,0.00009136061,0.00012556853,0.0027356753,0.24058662,0.0009486163,0.31630203,0.43499795,0.000893773],"about_ca_topic_score_codex":0.00016522579,"about_ca_topic_score_gemma":0.00009195458,"teacher_disagreement_score":0.74181134,"about_ca_system_score_codex":0.00021406483,"about_ca_system_score_gemma":0.00075461576,"threshold_uncertainty_score":0.9999491},"labels":[],"label_agreement":null}]}