{"meta":{"query_hash":"a99c1240e7e7","filters":{"venue":"Journal of Experimental & Theoretical Artificial Intelligence"},"cohort_total":17,"direct_labels_cover":0,"predictions_cover":17,"exported":17,"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/a99c1240e7e7","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Experimental+%26+Theoretical+Artificial+Intelligence"},"results":[{"id":"W1964479549","doi":"10.1080/0952813021000055162","title":"Cognitive evolutionary psychology without representational nativism","year":2003,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Cognition; Organism; Psychological nativism; Cognitive psychology; Cognitive science; Evolutionary psychology; Selection (genetic algorithm); Animal cognition; Psychology; Biology; Computer science; Social psychology; Genetics; Artificial intelligence; Neuroscience","score_opus":0.06384524336725118,"score_gpt":0.42907471272528974,"score_spread":0.36522946935803857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964479549","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04810406,0.017920146,0.41300753,0.101921305,0.0017222199,0.000105422725,0.00042780265,0.00039536672,0.4163961],"genre_scores_gemma":[0.8722619,0.005433255,0.0759546,0.00786821,0.0013720464,0.00024214106,0.00027093827,0.00014261698,0.036454365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99876225,0.00043220507,0.00004997352,0.00036363344,0.0002812876,0.00011058346],"domain_scores_gemma":[0.9985677,0.00050094497,0.0001156502,0.00044784968,0.00025042004,0.00011729567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022800735,0.0006066516,0.0006273533,0.000708839,0.0010509067,0.003436033,0.0012190035,0.002070477,0.005887998],"category_scores_gemma":[0.0039038663,0.000290452,0.0007131365,0.00036324898,0.011666138,0.007823144,0.0024480694,0.0043696226,0.0009524154],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049378386,0.0000042290876,0.000099884215,0.00002318298,0.000012735286,0.000014739134,0.000092284106,0.00051328907,0.00009144866,0.99479455,0.00067068875,0.0036780674],"study_design_scores_gemma":[0.0000042594515,0.0000047491435,0.00011293369,0.000009935099,0.0000036632555,0.000028359014,0.000024509092,0.0007193532,0.000045288845,0.99272835,0.0063149747,0.000003643994],"about_ca_topic_score_codex":0.0015500647,"about_ca_topic_score_gemma":0.00092826324,"teacher_disagreement_score":0.005887998,"about_ca_system_score_codex":0.0021132706,"about_ca_system_score_gemma":0.00085858844,"threshold_uncertainty_score":0.019697249},"labels":[],"label_agreement":null},{"id":"W1964840444","doi":"10.1080/0952813021000028621","title":"A case study in the meta-reasoning procedure ND","year":2003,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Case-based reasoning; Artificial intelligence","score_opus":0.06558557338037674,"score_gpt":0.34294391481825426,"score_spread":0.2773583414378775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964840444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24813527,0.00256782,0.6726429,0.009319089,0.00022077496,0.00066858134,0.00037909203,0.001639614,0.06442678],"genre_scores_gemma":[0.5363581,0.00053329545,0.45586914,0.0005126872,0.000063810396,0.00027817968,0.00016823267,0.00018565379,0.0060309186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99216753,0.005305996,0.0002718628,0.00059301464,0.00135034,0.00031114428],"domain_scores_gemma":[0.97792196,0.018406123,0.00034467093,0.0022091665,0.00083571253,0.00028222072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006719293,0.0006805187,0.0006157532,0.0009742898,0.0021211808,0.0028826445,0.0021519247,0.0035532198,0.0044714673],"category_scores_gemma":[0.0275412,0.00051192427,0.0012226601,0.001424001,0.0027541432,0.0047711744,0.0025943716,0.0037783503,0.0005871447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001753138,0.0016007238,0.008795504,0.0013433656,0.00016159739,0.006463591,0.00682954,0.08827799,0.017475922,0.52834654,0.009055737,0.3298963],"study_design_scores_gemma":[0.0007842631,0.0010280687,0.0037881942,0.0004998659,0.0001691359,0.006889084,0.0038435308,0.49352416,0.05072963,0.28806078,0.1505236,0.00015971174],"about_ca_topic_score_codex":0.003349701,"about_ca_topic_score_gemma":0.0045979647,"teacher_disagreement_score":0.006719293,"about_ca_system_score_codex":0.0023300247,"about_ca_system_score_gemma":0.0013324452,"threshold_uncertainty_score":0.035535455},"labels":[],"label_agreement":null},{"id":"W2012971775","doi":"10.1080/0952813x.2012.721010","title":"Naive Bayes text classifiers: a locally weighted learning approach","year":2012,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Naive Bayes classifier; Computer science; Artificial intelligence; Machine learning; Conditional independence; Bayes error rate; Bayesian programming; Bayes' theorem; Benchmark (surveying); Complement (music); Bayes classifier; Bayesian probability; Support vector machine; Bayes factor","score_opus":0.036113385853511676,"score_gpt":0.3060859303784622,"score_spread":0.26997254452495056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012971775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058195987,0.0010329804,0.98882675,0.00041116402,0.00014246709,0.0003434066,0.00027909613,0.0014290655,0.0017154478],"genre_scores_gemma":[0.19332926,0.0010869127,0.7942524,0.0009873775,0.0008124894,0.0011559505,0.0020196012,0.0003061264,0.0060499525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9906984,0.003562261,0.00073127256,0.0015262563,0.003203726,0.00027813556],"domain_scores_gemma":[0.98939985,0.0056844354,0.0007626645,0.0010629129,0.0029077786,0.00018236466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070018196,0.001987149,0.0031221001,0.005481208,0.0014534357,0.00289259,0.0050245533,0.0030288717,0.0040486427],"category_scores_gemma":[0.024739508,0.0007887874,0.0015649147,0.0045854156,0.0013425204,0.006020168,0.0018075178,0.0029598442,0.0033276002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041010496,0.00035346215,0.003629642,0.0006069218,0.00037066333,0.00020413943,0.0002766852,0.07446691,0.0052057216,0.017966853,0.017327702,0.8791811],"study_design_scores_gemma":[0.00009218669,0.00020290812,0.00086928764,0.00013752462,0.00018265503,0.000299779,0.0001155762,0.89903957,0.004123312,0.085331395,0.0095339315,0.00007193008],"about_ca_topic_score_codex":0.003620075,"about_ca_topic_score_gemma":0.0045931027,"teacher_disagreement_score":0.0070018196,"about_ca_system_score_codex":0.0014632368,"about_ca_system_score_gemma":0.0018187567,"threshold_uncertainty_score":0.037029564},"labels":[],"label_agreement":null},{"id":"W2015688842","doi":"10.1080/09528130903010295","title":"Warning: statistical benchmarking is addictive. Kicking the habit in machine learning","year":2009,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"Benchmarking; Computer science; Addiction; Set (abstract data type); Habit; Machine learning; Test (biology); Artificial intelligence; Measure (data warehouse); Psychology; Data mining; Social psychology; Psychiatry; Management","score_opus":0.02424670070729992,"score_gpt":0.3166015831447657,"score_spread":0.2923548824374658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015688842","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004438136,0.0024981285,0.008406499,0.93850803,0.046619378,0.000048495807,0.00017301735,0.00081425946,0.002488388],"genre_scores_gemma":[0.0053515835,0.0011559651,0.010266568,0.94737506,0.027704021,0.00014460822,0.00011242783,0.0004524738,0.0074373535],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9294394,0.030090945,0.006752445,0.003888368,0.028734034,0.001094685],"domain_scores_gemma":[0.6040722,0.2607274,0.016885525,0.022388916,0.087508775,0.008417167],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06696237,0.0016950624,0.0024461392,0.003052777,0.0033274244,0.007971239,0.004899037,0.026123181,0.0075742044],"category_scores_gemma":[0.35726863,0.0012405354,0.001941999,0.0036679914,0.014192147,0.010303257,0.0043083155,0.055188693,0.012853844],"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":[0.00007235425,0.00004498194,0.0006080634,0.00021545195,0.000037904938,0.000079597805,0.0002281417,0.00013523511,0.00027057173,0.012860011,0.96639997,0.019047685],"study_design_scores_gemma":[0.00021290434,0.00029107925,0.0027894366,0.0015665118,0.000067003544,0.0009387475,0.0005480668,0.0029752287,0.0012499093,0.10349646,0.88559824,0.0002664354],"about_ca_topic_score_codex":0.0028628567,"about_ca_topic_score_gemma":0.0036333774,"teacher_disagreement_score":0.93303764,"about_ca_system_score_codex":0.0024819742,"about_ca_system_score_gemma":0.006002941,"threshold_uncertainty_score":0.3541351},"labels":[],"label_agreement":null},{"id":"W2029216481","doi":"10.1080/0952813x.2015.1020574","title":"Mixed continuous/binary quantum-inspired learning system with non-negative least square optimisation for automated design of regularised ensemble extreme learning machines","year":2015,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Binary number; Evolutionary algorithm; Extreme learning machine; Ensemble learning; Quantum; Quantum computer; Artificial intelligence; Theoretical computer science; Algorithm; Mathematics; Artificial neural network","score_opus":0.061536752178799656,"score_gpt":0.2997632748872603,"score_spread":0.23822652270846062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029216481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013349646,0.00014155106,0.9842508,0.00008569164,0.00002358213,0.000024827508,0.000007852277,0.00013634622,0.001979705],"genre_scores_gemma":[0.6225758,0.00013277245,0.37353382,0.00017490283,0.00002657828,0.00022505414,0.000041805248,0.0000516818,0.0032375313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958354,0.00013971247,0.000021304571,0.0000831207,0.00013656497,0.000035729452],"domain_scores_gemma":[0.9995951,0.00020200307,0.000053968804,0.000048851656,0.000075497,0.0000244972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092706253,0.0004192958,0.00063246104,0.0002627341,0.00035628118,0.0006939397,0.0009949579,0.0008711004,0.0016492333],"category_scores_gemma":[0.0016567301,0.00035908888,0.0005414222,0.00027360633,0.0006620936,0.0006315879,0.000956062,0.0008695795,0.00033287104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064326894,0.000046531524,0.000499174,0.00008517688,0.000053717566,0.00007629405,0.000078242716,0.9178542,0.008882955,0.026894996,0.00049673975,0.04496763],"study_design_scores_gemma":[0.000004433175,0.000020158368,0.00002508716,0.0000026082353,0.0000033620552,0.000008984251,0.000002078921,0.997235,0.0005222105,0.0018648291,0.00030803063,0.0000031956338],"about_ca_topic_score_codex":0.0005900118,"about_ca_topic_score_gemma":0.00073273585,"teacher_disagreement_score":0.0016492333,"about_ca_system_score_codex":0.0004122794,"about_ca_system_score_gemma":0.00046699474,"threshold_uncertainty_score":0.0055172443},"labels":[],"label_agreement":null},{"id":"W2039928111","doi":"10.1080/09528131003713002","title":"A framework to support qualitative reasoning about COAs in a dynamic spatial environment","year":2010,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","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":"Université Laval","funders":"","keywords":"Computer science; Qualitative reasoning; Action (physics); Spatial intelligence; Space (punctuation); Human–computer interaction; Model-based reasoning; Conceptual framework; Artificial intelligence; Data science; Knowledge representation and reasoning; Epistemology","score_opus":0.022001612163320552,"score_gpt":0.35960123399109456,"score_spread":0.33759962182777403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039928111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006673618,0.00005469887,0.99639773,0.00037698765,0.000023072029,0.00008699465,0.0001461611,0.0003215481,0.0019253205],"genre_scores_gemma":[0.036010228,0.00015467829,0.96215963,0.00010890391,0.00003679907,0.00037040943,0.00037248596,0.00007648424,0.0007103618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705935,0.0011784298,0.00033314255,0.00045921354,0.000732601,0.00023724175],"domain_scores_gemma":[0.99470705,0.002608636,0.0005604339,0.00095202605,0.0008123378,0.00035959357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068995925,0.0015812682,0.0011059447,0.0029741218,0.0021634684,0.004807206,0.004998567,0.002177415,0.007921578],"category_scores_gemma":[0.012362655,0.0011754748,0.004154772,0.0027824529,0.004793722,0.0057640984,0.0058276877,0.0037667986,0.0011394133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018483055,0.000047701284,0.00024347231,0.00015752335,0.00004801154,0.00023671084,0.0005223902,0.080824815,0.00072664913,0.90078855,0.0015950388,0.014790684],"study_design_scores_gemma":[0.000052006173,0.00004133356,0.000111881614,0.00013431288,0.000051330328,0.00018097144,0.00033354716,0.33204296,0.0009723258,0.63504916,0.030976368,0.000053813634],"about_ca_topic_score_codex":0.02238425,"about_ca_topic_score_gemma":0.020100594,"teacher_disagreement_score":0.02238425,"about_ca_system_score_codex":0.002825852,"about_ca_system_score_gemma":0.0047095655,"threshold_uncertainty_score":0.04450792},"labels":[],"label_agreement":null},{"id":"W2040248557","doi":"10.1080/0952813x.2012.693686","title":"Modelling and simulating early stopping of RCTs: a case study of early stop due to harm","year":2012,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interim; Harm; Early stopping; Computer science; Randomized controlled trial; Set (abstract data type); Publication; Interim analysis; Operations research; Medical physics; Risk analysis (engineering); Medicine; Artificial intelligence; Law; Political science","score_opus":0.6478130151682211,"score_gpt":0.535393356659427,"score_spread":0.1124196585087941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040248557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3156348,0.0084429635,0.61873966,0.02647564,0.00057099137,0.0056174104,0.0021118233,0.00058352394,0.021823244],"genre_scores_gemma":[0.70337105,0.0022978417,0.2857477,0.0016451764,0.00014867757,0.004359737,0.00042280287,0.00006740458,0.0019395476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90954334,0.08287075,0.0026796018,0.0016137783,0.002117525,0.0011749206],"domain_scores_gemma":[0.36853278,0.6088605,0.009940244,0.0071076322,0.0042752954,0.0012835669],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09344422,0.0019028924,0.003115001,0.0025966454,0.001630479,0.0037152977,0.004191389,0.009530616,0.0039969035],"category_scores_gemma":[0.24968988,0.0016754685,0.0067219036,0.003307551,0.0034360918,0.0029483065,0.003002497,0.006502415,0.00034679804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001674574,0.00041902732,0.008942324,0.0012907301,0.0009437682,0.0012632713,0.0019450729,0.8969233,0.00033882953,0.062412925,0.0011538202,0.022692297],"study_design_scores_gemma":[0.002098635,0.0013972407,0.0019664108,0.0007777687,0.0011036748,0.00041293396,0.00066055416,0.8652209,0.00069629966,0.11918371,0.0063273385,0.0001546373],"about_ca_topic_score_codex":0.01604507,"about_ca_topic_score_gemma":0.014893729,"teacher_disagreement_score":0.9065558,"about_ca_system_score_codex":0.004870629,"about_ca_system_score_gemma":0.007129042,"threshold_uncertainty_score":0.49418616},"labels":[],"label_agreement":null},{"id":"W2055186188","doi":"10.1080/0952813x.2010.535706","title":"Testing variation of attention capacities in a complex auto-adaptive system: a Stroop task simulation","year":2011,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Stroop effect; Computer science; Task (project management); Variation (astronomy); Cognition; Cognitive psychology; Artificial intelligence; Psychology","score_opus":0.15435785224986065,"score_gpt":0.3222387473193616,"score_spread":0.16788089506950093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055186188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98583305,0.000013996411,0.013051019,0.000031444,0.0000069689513,0.000042977008,0.00004336525,0.00003263587,0.0009446502],"genre_scores_gemma":[0.99683005,0.000009037048,0.002876905,0.0000087030285,0.000001328676,0.000048265272,0.00003850275,0.000005182769,0.00018203471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966776,0.00017378503,0.00002430782,0.00004000236,0.00005394731,0.000040146708],"domain_scores_gemma":[0.99697745,0.0021864916,0.00021475254,0.00034856846,0.00014354856,0.00012916826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063177943,0.00036585308,0.00024038763,0.00032862593,0.0001403414,0.00046139339,0.0004938563,0.00036914452,0.001376197],"category_scores_gemma":[0.0038407338,0.00014696478,0.0003641977,0.0001554828,0.0005539028,0.00040600318,0.00072412536,0.00039311175,0.00010024833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015816117,0.0017034326,0.022945946,0.000192176,0.0003415372,0.00043302367,0.00082910404,0.88137275,0.067442045,0.009101807,0.00058439944,0.013472133],"study_design_scores_gemma":[0.00017863438,0.0013168616,0.01165641,0.000012791278,0.00005113682,0.00007528871,0.00009735756,0.9685047,0.011308742,0.0061560133,0.00061332795,0.000028630277],"about_ca_topic_score_codex":0.0018287627,"about_ca_topic_score_gemma":0.00087149604,"teacher_disagreement_score":0.0018287627,"about_ca_system_score_codex":0.00040075948,"about_ca_system_score_gemma":0.0003079362,"threshold_uncertainty_score":0.0046038628},"labels":[],"label_agreement":null},{"id":"W2065798393","doi":"10.1080/09528130701475617","title":"Weighting strategy for non-clausal resolution","year":2008,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Weighting; Satisfiability; Resolution (logic); Propositional formula; Propositional calculus; Algorithm; Propositional variable; Artificial intelligence; Theoretical computer science; Programming language","score_opus":0.06308542882193925,"score_gpt":0.32893391334340266,"score_spread":0.2658484845214634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065798393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015824109,0.00009191777,0.9953803,0.00009230147,0.000050053193,0.00010979086,0.000030437966,0.0003450727,0.0023176677],"genre_scores_gemma":[0.07020722,0.00029020425,0.92153543,0.00015681704,0.00006164808,0.0004180938,0.00021695966,0.00042510088,0.006688474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943183,0.0020577605,0.00047083315,0.00061456114,0.0022559895,0.00028261324],"domain_scores_gemma":[0.9943725,0.0029628363,0.00021948316,0.0011424845,0.001114771,0.0001879634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052934997,0.0013869368,0.0014193457,0.0024487388,0.0013124401,0.002711034,0.0035114083,0.0015490848,0.01313935],"category_scores_gemma":[0.015249738,0.000879553,0.001547634,0.0020273717,0.0015281425,0.005192478,0.0041016275,0.0031241209,0.0031856874],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110421795,0.000210704,0.0003017184,0.0003875301,0.000096157426,0.00025233123,0.00029370663,0.021093922,0.009919082,0.63540065,0.003938852,0.32799494],"study_design_scores_gemma":[0.00007693847,0.00007890451,0.00014421344,0.00010636623,0.00008659488,0.00026826997,0.00009831425,0.31709245,0.014727548,0.6479748,0.019274369,0.0000712308],"about_ca_topic_score_codex":0.0010443039,"about_ca_topic_score_gemma":0.0018827172,"teacher_disagreement_score":0.01313935,"about_ca_system_score_codex":0.0013031021,"about_ca_system_score_gemma":0.0019789068,"threshold_uncertainty_score":0.043955445},"labels":[],"label_agreement":null},{"id":"W2080075719","doi":"10.1080/0952813x.2014.924588","title":"Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission","year":2014,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automotive engine; Computer science; Automotive industry; Artificial neural network; Identification (biology); SPARK (programming language); Gaussian; Coupling (piping); Algorithm; Machine learning; Artificial intelligence; Automotive engineering; Mechanical engineering; Engineering; Aerospace engineering","score_opus":0.014943793213099646,"score_gpt":0.2809704708934667,"score_spread":0.2660266776803671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080075719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18464819,0.0006856813,0.81117004,0.00018005827,0.000053867672,0.00004620439,0.00002174005,0.0005321911,0.0026620368],"genre_scores_gemma":[0.9299198,0.000119815595,0.06813836,0.00007046363,0.000011459032,0.000045910896,0.00003328803,0.000026077563,0.0016348865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979407,0.00006359296,0.000010345557,0.000050683997,0.00005040856,0.00003085495],"domain_scores_gemma":[0.99960095,0.0002196618,0.00005241378,0.000024776846,0.00008972197,0.000012464547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000719568,0.0004998651,0.000460053,0.00044367102,0.00016943853,0.00043647,0.00067906035,0.0007350641,0.0004521107],"category_scores_gemma":[0.0016277924,0.0001864226,0.00037837503,0.00029568552,0.0003189955,0.00044411674,0.0004029991,0.00046742335,0.00010233908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009047002,0.000055322573,0.0019513424,0.000052234496,0.000070876646,0.00007608349,0.0000727928,0.92506725,0.008471472,0.0017660847,0.0002811514,0.062044848],"study_design_scores_gemma":[0.000001923295,0.00001983005,0.00019571949,0.000001665761,0.000004054736,0.0000066321763,0.0000030942447,0.9990295,0.0004542816,0.00022042825,0.00006075076,0.0000021029718],"about_ca_topic_score_codex":0.003241668,"about_ca_topic_score_gemma":0.002992439,"teacher_disagreement_score":0.003241668,"about_ca_system_score_codex":0.000307158,"about_ca_system_score_gemma":0.0003446529,"threshold_uncertainty_score":0.0064455867},"labels":[],"label_agreement":null},{"id":"W2089872474","doi":"10.1080/0952813031000064567","title":"Planning under uncertainty as G<scp>OLOG</scp>programs","year":2003,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Situation calculus; Computer science; Logic programming; Artificial intelligence; Popularity; Belief revision; Programming language","score_opus":0.044964826564468104,"score_gpt":0.32627017792663426,"score_spread":0.28130535136216617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089872474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0553884,0.0015388331,0.84044224,0.005515096,0.000097073076,0.00031651778,0.0010481104,0.0026594829,0.092994176],"genre_scores_gemma":[0.6142391,0.0020655429,0.34940034,0.0008487697,0.00015680131,0.0005396719,0.0014262423,0.0004433739,0.0308802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99924195,0.00021711474,0.00005289299,0.00010749168,0.00027977454,0.0001007636],"domain_scores_gemma":[0.9985661,0.0008218018,0.00017280497,0.00016715098,0.00020153895,0.00007062899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001132591,0.00046954234,0.00042149713,0.0014233536,0.00082029076,0.004379477,0.0011289513,0.0010704263,0.006021869],"category_scores_gemma":[0.0035000092,0.00047890015,0.00090513687,0.0029809903,0.0033484432,0.0030678893,0.0013679359,0.0017112902,0.0010246753],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039623348,0.000022368604,0.0005225469,0.00008845031,0.000026544458,0.0003563409,0.0005231776,0.07233223,0.00047679796,0.89412934,0.004196164,0.027286407],"study_design_scores_gemma":[0.000022522365,0.000012675972,0.00020185423,0.000048128466,0.000019012237,0.00009988878,0.000122029094,0.16163087,0.00051639706,0.8084777,0.028834235,0.000014656532],"about_ca_topic_score_codex":0.017571976,"about_ca_topic_score_gemma":0.010915977,"teacher_disagreement_score":0.017571976,"about_ca_system_score_codex":0.0032329499,"about_ca_system_score_gemma":0.0025989383,"threshold_uncertainty_score":0.03493941},"labels":[],"label_agreement":null},{"id":"W2570355113","doi":"10.1080/0952813x.2016.1264088","title":"Comparisons of several variants of continuous quantum-inspired evolutionary algorithms","year":2017,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scalability; Robustness (evolution); Evolutionary algorithm; Chaotic; Curse of dimensionality; Quantum; Convergence (economics); Algorithm; Exploit; Context (archaeology); Sensitivity (control systems); Domain (mathematical analysis); Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.04356117225472432,"score_gpt":0.32707887949357856,"score_spread":0.2835177072388542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570355113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49660376,0.0077454816,0.46845874,0.0005513839,0.00033259857,0.00031346665,0.00019466622,0.00048490535,0.025315022],"genre_scores_gemma":[0.8958443,0.0011513034,0.10163316,0.000060435228,0.000026237713,0.00013839423,0.00012360422,0.000042670457,0.0009798368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999469,0.00019615133,0.000042861677,0.000058221656,0.00019152327,0.000042302578],"domain_scores_gemma":[0.99829596,0.0010873568,0.00008551866,0.0001836294,0.00028365344,0.00006373793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015411194,0.0004999928,0.000723966,0.0010175384,0.00042150117,0.0009625701,0.0012523768,0.0009174979,0.0014058555],"category_scores_gemma":[0.0053895344,0.00016347934,0.000551754,0.0011011154,0.0006062106,0.0010760188,0.0006387274,0.00059241883,0.00014616818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026832725,0.00017517696,0.0032861882,0.00044873985,0.00022919007,0.00012491569,0.000121177785,0.8649408,0.0039083557,0.03290539,0.0006526294,0.09293902],"study_design_scores_gemma":[0.000033900993,0.00018253288,0.00093914324,0.000018416838,0.000034528082,0.00007104019,0.000039420163,0.99383116,0.0010501192,0.0027133331,0.0010713121,0.000015035128],"about_ca_topic_score_codex":0.0018445565,"about_ca_topic_score_gemma":0.0018305628,"teacher_disagreement_score":0.0018445565,"about_ca_system_score_codex":0.0005843435,"about_ca_system_score_gemma":0.0006388607,"threshold_uncertainty_score":0.008150339},"labels":[],"label_agreement":null},{"id":"W2772583926","doi":"10.1080/0952813x.2017.1413140","title":"Reproducible research: a minority opinion","year":2017,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Argument (complex analysis); Computer science; Interpretation (philosophy); Code (set theory); Epistemology; Scientific misconduct; Engineering ethics; Data science; Set (abstract data type); Philosophy","score_opus":0.565419335672116,"score_gpt":0.5673036619183319,"score_spread":0.0018843262462159283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772583926","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071407645,0.01587906,0.0022809738,0.9705922,0.006072388,0.000024215467,0.000045995195,0.000029642344,0.004361518],"genre_scores_gemma":[0.057971388,0.024448719,0.005564356,0.87061566,0.037052132,0.00023585705,0.00016138138,0.00017199466,0.0037785566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.84017485,0.05884573,0.015804289,0.028698817,0.051090583,0.0053857346],"domain_scores_gemma":[0.44800165,0.4033819,0.020307938,0.032267097,0.078764595,0.017276756],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23565814,0.00077769055,0.0028915945,0.004312314,0.009032742,0.022199987,0.00931413,0.0375204,0.006643957],"category_scores_gemma":[0.40289548,0.0009913193,0.0023886508,0.005061854,0.037744004,0.028570872,0.012122384,0.042523224,0.0034050357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022877978,0.00012918751,0.0028035673,0.0018216909,0.00019962485,0.00040953484,0.0062641683,0.0003079984,0.000634332,0.52743053,0.3115047,0.14826584],"study_design_scores_gemma":[0.00011817359,0.00010131026,0.0017051835,0.0036501659,0.00010745688,0.00039420326,0.003695691,0.0006544605,0.0004916922,0.27240604,0.7165631,0.00011247399],"about_ca_topic_score_codex":0.004467436,"about_ca_topic_score_gemma":0.0026261678,"teacher_disagreement_score":0.76434183,"about_ca_system_score_codex":0.009443807,"about_ca_system_score_gemma":0.020598145,"threshold_uncertainty_score":0.9425696},"labels":[],"label_agreement":null},{"id":"W3002919848","doi":"10.1080/0952813x.2020.1716857","title":"Communicative bottlenecks lead to maximal information transfer","year":2020,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Partition (number theory); Information transfer; Signalling; Theoretical computer science; Simple (philosophy); Stability (learning theory); Property (philosophy); Empirical evidence; Mathematical economics; Machine learning; Mathematics; Telecommunications","score_opus":0.16713581174209288,"score_gpt":0.41773369890913226,"score_spread":0.25059788716703935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002919848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50505775,0.00035262137,0.45397416,0.002110248,0.00009834079,0.00013387477,0.00015723356,0.000443423,0.03767244],"genre_scores_gemma":[0.986524,0.000095940886,0.011638412,0.00012771267,0.000019451778,0.00006881741,0.000023644257,0.00004494832,0.001457155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983689,0.0007588966,0.000060805214,0.00019391645,0.00027373296,0.0003437499],"domain_scores_gemma":[0.98001343,0.015693542,0.0016442785,0.00088316214,0.0005918014,0.0011737273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032032384,0.0006416549,0.0011935988,0.0010629719,0.0011504606,0.002563219,0.0012679084,0.0015930565,0.007830079],"category_scores_gemma":[0.03340008,0.0006216431,0.0011886633,0.0003513294,0.00323716,0.0057694134,0.002251859,0.0025623888,0.00050684606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030037717,0.00012400879,0.0010876571,0.0001920902,0.00006183395,0.0002883192,0.0005183241,0.23649807,0.005847873,0.7480126,0.001497301,0.005571582],"study_design_scores_gemma":[0.00007344525,0.00008088429,0.00056885445,0.000030200652,0.00002688921,0.000095257004,0.00020475118,0.52710533,0.0015313225,0.46948472,0.00076151016,0.00003683379],"about_ca_topic_score_codex":0.0015537929,"about_ca_topic_score_gemma":0.0007142131,"teacher_disagreement_score":0.007830079,"about_ca_system_score_codex":0.0019769047,"about_ca_system_score_gemma":0.0012269416,"threshold_uncertainty_score":0.026194215},"labels":[],"label_agreement":null},{"id":"W4212937252","doi":"10.1080/0952813x.2021.1960630","title":"HLA: a novel hybrid model based on fixed structure and variable structure learning automata","year":2022,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Learning automata; Variable (mathematics); Dropout (neural networks); Convergence (economics); Automaton; Artificial intelligence; Artificial neural network; Margin (machine learning); Stability (learning theory); Machine learning; Mathematics","score_opus":0.022573203067814802,"score_gpt":0.2877095505486438,"score_spread":0.265136347480829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212937252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037641186,0.00048518126,0.9549971,0.0004644265,0.00013568881,0.0000668177,0.0001834216,0.00087289914,0.005153227],"genre_scores_gemma":[0.93452567,0.00033153803,0.057345312,0.00016237613,0.00004339002,0.00023783038,0.00013513624,0.000048164195,0.0071706492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996234,0.00007861082,0.000028215518,0.000117619835,0.0000891651,0.00006290239],"domain_scores_gemma":[0.9994411,0.00024166654,0.00008297849,0.00005639366,0.00012714739,0.000050822517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850164,0.00049511925,0.0008763507,0.0004783344,0.00040118108,0.0011050865,0.0019042143,0.0011503061,0.003256962],"category_scores_gemma":[0.0013973576,0.00034414328,0.0007964916,0.0004029342,0.00094767497,0.0016630238,0.0011087523,0.0011892244,0.0003981121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009798149,0.00004742388,0.001176704,0.000084004605,0.00005562038,0.00013212416,0.000089455454,0.9314929,0.0032697006,0.03163677,0.0009665866,0.030950667],"study_design_scores_gemma":[0.000009465904,0.000036572546,0.00006972791,0.00000413297,0.000008635258,0.000018701014,0.000003859853,0.9952636,0.00023586668,0.0039005417,0.00044414063,0.000004748747],"about_ca_topic_score_codex":0.004781304,"about_ca_topic_score_gemma":0.003500024,"teacher_disagreement_score":0.004781304,"about_ca_system_score_codex":0.0007598179,"about_ca_system_score_gemma":0.0010883036,"threshold_uncertainty_score":0.010895669},"labels":[],"label_agreement":null},{"id":"W4293052826","doi":"10.1080/0952813x.2022.2080868","title":"Blind separation of speech from aortic regurgitation signals using Dhoulath’s method","year":2022,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Blind Source Separation Techniques","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":"Brampton Civic Hospital","funders":"","keywords":"Computer science; Separation (statistics); Speech recognition; Regurgitation (circulation); Blind signal separation; Telecommunications; Cardiology; Medicine; Machine learning","score_opus":0.07129861354360725,"score_gpt":0.4128806467085715,"score_spread":0.34158203316496427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293052826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033614937,0.0005908167,0.9621944,0.00010936955,0.00015164676,0.00014361613,0.000085204876,0.00030559054,0.0028043913],"genre_scores_gemma":[0.24295498,0.0009766044,0.74835646,0.00010997786,0.00007790228,0.0001889951,0.00016214087,0.000075113196,0.007097922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993375,0.00020713998,0.000044232085,0.00014032617,0.00022505663,0.000045823956],"domain_scores_gemma":[0.9992873,0.0003947974,0.00005201249,0.00008728065,0.00014989293,0.000028682416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007404411,0.00061024557,0.0004116783,0.0009009024,0.00034958805,0.0006056397,0.00045878717,0.0007449108,0.0025651373],"category_scores_gemma":[0.0018172346,0.00020518563,0.0006624136,0.00055306294,0.00061299175,0.00080339436,0.00070964807,0.00067828386,0.0010501101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011901525,0.00020501378,0.001276952,0.0005859155,0.00014442278,0.00033379145,0.0005123671,0.0067614755,0.32587743,0.013686059,0.001403343,0.64802307],"study_design_scores_gemma":[0.00028291406,0.0014109549,0.010069034,0.00010873887,0.0002771284,0.0038870578,0.0004118712,0.3310588,0.6035993,0.015731042,0.03288611,0.00027714265],"about_ca_topic_score_codex":0.0006456791,"about_ca_topic_score_gemma":0.0007078659,"teacher_disagreement_score":0.0025651373,"about_ca_system_score_codex":0.00022229868,"about_ca_system_score_gemma":0.00052760466,"threshold_uncertainty_score":0.008581281},"labels":[],"label_agreement":null},{"id":"W4321369875","doi":"10.1080/0952813x.2023.2178516","title":"The future of endoscopy – what are the thoughts on artificial intelligence?","year":2023,"lang":"en","type":"article","venue":"Journal of Experimental & Theoretical Artificial Intelligence","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":6,"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":"Endoscopy; Healthcare system; Medicine; Quality (philosophy); Quarter (Canadian coin); Computer science; Quality assurance; Health care; Radiology; Pathology; External quality assessment","score_opus":0.040798551291883534,"score_gpt":0.3500763572420568,"score_spread":0.3092778059501733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321369875","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011308822,0.33161882,0.00060589955,0.65269506,0.008841986,0.000005456911,0.00003187437,0.00002512349,0.005044872],"genre_scores_gemma":[0.07312033,0.6300954,0.0023742283,0.2302092,0.057702996,0.000050452396,0.00008463848,0.00006878219,0.0062939986],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.9956241,0.002793371,0.00017739659,0.0003182753,0.0008463913,0.00024040403],"domain_scores_gemma":[0.9771262,0.016005872,0.00085229333,0.00090817304,0.0026309073,0.0024765106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010212645,0.00072516326,0.0010913741,0.0023335146,0.0021207752,0.009989552,0.0015434267,0.0066821133,0.005498724],"category_scores_gemma":[0.017829064,0.00045111234,0.0009730448,0.0014633391,0.018805685,0.016522488,0.0019548433,0.012455669,0.0021276553],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037856973,0.00014304231,0.003731558,0.0034116004,0.00016137911,0.0005809482,0.007121532,0.00059231516,0.0006129991,0.12934455,0.51119846,0.342723],"study_design_scores_gemma":[0.000039951,0.00018445833,0.0039794757,0.0070325774,0.000049230304,0.0013518557,0.013938175,0.0005152602,0.00019539715,0.19910283,0.7734645,0.0001463739],"about_ca_topic_score_codex":0.0031315042,"about_ca_topic_score_gemma":0.0038420672,"teacher_disagreement_score":0.010212645,"about_ca_system_score_codex":0.0029777042,"about_ca_system_score_gemma":0.0028693967,"threshold_uncertainty_score":0.054010272},"labels":[],"label_agreement":null}]}