{"meta":{"query_hash":"c79f7aa28582","filters":{"venue":"Cognitive Systems Research"},"cohort_total":30,"direct_labels_cover":0,"predictions_cover":30,"exported":30,"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/c79f7aa28582","api":"https://metacan.xera.ac/api/v1/cohort?venue=Cognitive+Systems+Research"},"results":[{"id":"W155118278","doi":"10.1016/j.cogsys.2005.05.002","title":"","year":2005,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Psychology","score_opus":0.5492937920249916,"score_gpt":0.5038071346569903,"score_spread":0.04548665736800128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W155118278","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045847665,0.003112494,0.014110276,0.0069755865,0.0020503993,0.000041819083,0.0005738092,0.00053374254,0.96801704],"genre_scores_gemma":[0.054286245,0.003840307,0.005684834,0.0013458843,0.00066668185,0.000048254584,0.00084222126,0.000118866956,0.93316674],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99985456,0.000017486705,0.000005690724,0.00003358227,0.00007328567,0.000015289921],"domain_scores_gemma":[0.99958664,0.000055077777,0.00001771669,0.00011713419,0.00014612771,0.000077265046],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003591887,0.00032938906,0.00019825224,0.00081748166,0.000814964,0.0020183367,0.00047619984,0.0007167694,0.21752004],"category_scores_gemma":[0.0010830066,0.00010550007,0.0001491829,0.00074365997,0.00092346483,0.0014846503,0.0007778736,0.0007789559,0.09395244],"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":[0.00007851868,0.00005559494,0.0007805963,0.00014752007,0.000008278042,0.00010971211,0.0003570597,0.00014574615,0.00490907,0.31558388,0.2717624,0.40606156],"study_design_scores_gemma":[0.000007336767,0.000019693409,0.0013503592,0.00006166461,0.000006923692,0.00029655153,0.00014437213,0.00014882763,0.0028636039,0.0661527,0.9289429,0.0000050480385],"about_ca_topic_score_codex":0.00094465143,"about_ca_topic_score_gemma":0.0016158734,"teacher_disagreement_score":0.78247994,"about_ca_system_score_codex":0.00074615696,"about_ca_system_score_gemma":0.0008259979,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1974685448","doi":"10.1016/j.cogsys.2005.03.002","title":"Agent communication pragmatics: the cognitive coherence approach","year":2005,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Pragmatics; Computer science; Semantics (computer science); Coherence (philosophical gambling strategy); Toolbox; Cognitive science; Syntax; Cognition; Artificial intelligence; Psychology; Linguistics; Programming language","score_opus":0.1632641581410741,"score_gpt":0.39595952450022753,"score_spread":0.23269536635915344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974685448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023037395,0.008396639,0.8085098,0.03156309,0.0007245309,0.00022506739,0.00019734679,0.00024628223,0.12709978],"genre_scores_gemma":[0.85357344,0.002534205,0.13561848,0.0013993442,0.0009752562,0.00046549126,0.00017698496,0.00020031551,0.005056572],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98627394,0.009154898,0.0006633276,0.0011418135,0.0021844504,0.00058145856],"domain_scores_gemma":[0.97066945,0.02260831,0.001501792,0.0022543923,0.00218256,0.0007834409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0135737965,0.0010792599,0.0016287947,0.003322335,0.003678191,0.011496829,0.0028401953,0.005043911,0.007995102],"category_scores_gemma":[0.04501947,0.0014155492,0.0015648529,0.0027964355,0.015294609,0.027402801,0.005855068,0.005485218,0.0008576245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028295888,0.00001803192,0.00017912603,0.00010632225,0.00003247461,0.00005358347,0.0022740203,0.0012466949,0.00014510634,0.987961,0.0007612185,0.0071940487],"study_design_scores_gemma":[0.000026557038,0.000012201816,0.00015935727,0.000043514323,0.000023242124,0.00004561928,0.00050836516,0.004262211,0.00008395713,0.9896938,0.0051273513,0.000013841431],"about_ca_topic_score_codex":0.003910856,"about_ca_topic_score_gemma":0.002185696,"teacher_disagreement_score":0.0135737965,"about_ca_system_score_codex":0.0041552116,"about_ca_system_score_gemma":0.0038908806,"threshold_uncertainty_score":0.07178593},"labels":[],"label_agreement":null},{"id":"W1981395566","doi":"10.1016/j.cogsys.2007.06.006","title":"Deconstructing and reconstructing ACT-R: Exploring the architectural space","year":2007,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Python (programming language); Computer science; Cognitive architecture; Architecture; Theoretical computer science; Cognitive science; Programming language; Space (punctuation); Syntax; Cognition; Computational model; Artificial intelligence; Psychology","score_opus":0.17915555726908625,"score_gpt":0.37995518361572184,"score_spread":0.2007996263466356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981395566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09669458,0.0005284765,0.8756631,0.0022539643,0.000076510376,0.000049684088,0.00036536204,0.0012093516,0.023158988],"genre_scores_gemma":[0.72017574,0.00040429583,0.2720565,0.0001924812,0.00002314472,0.000070582595,0.000679087,0.0005321636,0.005866022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998464,0.0010145714,0.000051869814,0.00026952304,0.00012359626,0.00007655254],"domain_scores_gemma":[0.9957675,0.002036815,0.0003055982,0.0013531687,0.0003448004,0.00019217581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021870858,0.0009632366,0.00047523604,0.0011546419,0.001143887,0.0045903022,0.0018380116,0.0011270661,0.0061641433],"category_scores_gemma":[0.014067416,0.0007524338,0.0011534393,0.00096921856,0.005554872,0.0075305407,0.0025707055,0.0026144336,0.0014975029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016049786,0.000046424615,0.008063867,0.00020846885,0.000069486596,0.00040573254,0.0040805927,0.068097904,0.0023882615,0.8024233,0.003000613,0.111054815],"study_design_scores_gemma":[0.000013595672,0.00002863376,0.0011111782,0.000052380034,0.000025920403,0.00018610881,0.0008443501,0.18055983,0.0014050152,0.8089371,0.006807808,0.00002803372],"about_ca_topic_score_codex":0.0070193727,"about_ca_topic_score_gemma":0.011776893,"teacher_disagreement_score":0.0070193727,"about_ca_system_score_codex":0.0010711816,"about_ca_system_score_gemma":0.0017856567,"threshold_uncertainty_score":0.020621061},"labels":[],"label_agreement":null},{"id":"W1992074915","doi":"10.1016/j.cogsys.2010.10.002","title":"Emotive and cognitive simulations by agents: Roles of three levels of information processing","year":2010,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Korea Institute for Advancement of Technology; Agency for Science, Technology and Research","keywords":"Emotive; Information processing; Cognition; Affect (linguistics); Perception; Information processing theory; Cognitive psychology; Psychology; Computer science; Cognitive science; Communication; Neuroscience","score_opus":0.08538346068067589,"score_gpt":0.3688044701746155,"score_spread":0.2834210094939396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992074915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3362307,0.00062909577,0.52153206,0.0030738756,0.00007374888,0.00028548707,0.00022595712,0.0007180289,0.13723107],"genre_scores_gemma":[0.9340355,0.00019202262,0.06283987,0.000120224984,0.000019933887,0.00013036019,0.00009240713,0.00006678012,0.0025029727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769384,0.0012179767,0.00012115771,0.00022683712,0.00052704103,0.00021313563],"domain_scores_gemma":[0.99151057,0.005492193,0.0005870296,0.001192494,0.00062794785,0.0005896628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021755283,0.00049704005,0.00042993922,0.0011795779,0.000975845,0.010149925,0.0018001051,0.0019772435,0.0059572617],"category_scores_gemma":[0.013777943,0.000635501,0.00076877594,0.0009549709,0.003576404,0.007415614,0.0025355371,0.0015864272,0.0006841811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003098303,0.00030710053,0.009132255,0.00030383113,0.00014031857,0.0002488168,0.010320407,0.023018753,0.009520983,0.8988118,0.0010232931,0.04686258],"study_design_scores_gemma":[0.00008270539,0.00018791168,0.011105811,0.00013213926,0.00016597038,0.00019720488,0.0037307385,0.18188,0.009673487,0.7838325,0.008897941,0.00011367946],"about_ca_topic_score_codex":0.0014514579,"about_ca_topic_score_gemma":0.0008160181,"teacher_disagreement_score":0.010149925,"about_ca_system_score_codex":0.0008597059,"about_ca_system_score_gemma":0.0010744869,"threshold_uncertainty_score":0.019929051},"labels":[],"label_agreement":null},{"id":"W2003570164","doi":"10.1016/j.cogsys.2008.08.001","title":"Brain activation detection by neighborhood one-class SVM","year":2008,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Support vector machine; Hyperplane; Pattern recognition (psychology); Artificial intelligence; Computer science; Cluster analysis; Fuzzy logic; Voxel; Kernel (algebra); Class (philosophy); Consistency (knowledge bases); Data mining; Mathematics","score_opus":0.10219537301556328,"score_gpt":0.3559179362765997,"score_spread":0.25372256326103637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003570164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16955103,0.0006499306,0.82679284,0.00019408879,0.000118676115,0.00007049684,0.00013677774,0.0009847039,0.0015014956],"genre_scores_gemma":[0.88203084,0.0001577027,0.1154005,0.0000497374,0.00006192807,0.00007660993,0.00022989637,0.00005980596,0.0019330103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954957,0.00010628961,0.000023553926,0.00015807817,0.00010496858,0.00005749111],"domain_scores_gemma":[0.9992539,0.00036690652,0.000060863178,0.00008888428,0.00019196057,0.000037528665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009427992,0.00045987216,0.0011983572,0.0008317822,0.0004475125,0.00082636625,0.0009215956,0.00089705683,0.0012309701],"category_scores_gemma":[0.0024490675,0.00023240724,0.00065778656,0.00060687104,0.00035239226,0.0008872832,0.00064175884,0.0007970637,0.00041313845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010913928,0.00035093605,0.016874336,0.00014981741,0.0002458476,0.0001976123,0.00015546057,0.05636204,0.039828114,0.004690811,0.0037962392,0.8762575],"study_design_scores_gemma":[0.000017387714,0.00011129776,0.0045053787,0.000007350264,0.0000374397,0.00017146385,0.000036740632,0.9837923,0.0070570577,0.0036783998,0.0005709643,0.0000142445915],"about_ca_topic_score_codex":0.0014790996,"about_ca_topic_score_gemma":0.0014074235,"teacher_disagreement_score":0.0014790996,"about_ca_system_score_codex":0.0002803502,"about_ca_system_score_gemma":0.00042334505,"threshold_uncertainty_score":0.0049860477},"labels":[],"label_agreement":null},{"id":"W2005603639","doi":"10.1016/j.cogsys.2014.09.001","title":"The role of analogy in ontology alignment: A study on LISA","year":2014,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Ontology; Computer science; Ontology alignment; Heuristics; Analogy; Process ontology; Context (archaeology); Ontology-based data integration; Interoperability; Upper ontology; Semantic Web; Process (computing); Information retrieval; Artificial intelligence; World Wide Web; Epistemology","score_opus":0.0776602751204351,"score_gpt":0.38603649335220147,"score_spread":0.30837621823176636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005603639","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":[0.7888552,0.00086343364,0.07925138,0.0017016006,0.00004871986,0.00016130532,0.000103513215,0.00022741537,0.12878747],"genre_scores_gemma":[0.9861373,0.0001764626,0.011309348,0.00008185786,0.000007292788,0.000052930194,0.00007445116,0.000080121165,0.0020802927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925012,0.005660305,0.0002793998,0.00055763585,0.0007523893,0.00024917562],"domain_scores_gemma":[0.9538809,0.03718925,0.0023153266,0.0037928175,0.0023255835,0.00049611926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064522517,0.00033338746,0.00061333505,0.0024300122,0.005586043,0.005048814,0.0015038111,0.0011793914,0.006188138],"category_scores_gemma":[0.055917814,0.00047613904,0.00046065726,0.0054387953,0.0054325648,0.01745738,0.0036608253,0.0021690908,0.00070078456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046574188,0.0006261924,0.042583063,0.0004880949,0.00006434054,0.0012873042,0.15539916,0.0035890806,0.0022906784,0.66316056,0.0019058819,0.12813984],"study_design_scores_gemma":[0.00013380992,0.0003600012,0.029134331,0.00048280967,0.00020520505,0.0025847366,0.16798563,0.08027387,0.0076867924,0.6588395,0.052177433,0.00013585169],"about_ca_topic_score_codex":0.005506443,"about_ca_topic_score_gemma":0.0049447846,"teacher_disagreement_score":0.0064522517,"about_ca_system_score_codex":0.0023363016,"about_ca_system_score_gemma":0.0033343977,"threshold_uncertainty_score":0.034123182},"labels":[],"label_agreement":null},{"id":"W2008170736","doi":"10.1016/j.cogsys.2007.05.004","title":"The cultural evolution of socially situated cognition","year":2007,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Situated; Darwinism; Survival of the fittest; Natural selection; Cognitive science; Cognition; Selection (genetic algorithm); Inheritance (genetic algorithm); Autopoiesis; Natural (archaeology); Adaptation (eye); Process (computing); Population; Epistemology; Psychology; Niche construction; Sociocultural evolution; Cognitive psychology; Sociology; Computer science; Biology; Evolutionary biology; Artificial intelligence; Neuroscience; Philosophy; Genetics","score_opus":0.10931046360866921,"score_gpt":0.46154185451144114,"score_spread":0.35223139090277195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008170736","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":[0.841137,0.0032833542,0.039054856,0.0072564525,0.00011713564,0.000044528755,0.00007950898,0.000045463945,0.10898171],"genre_scores_gemma":[0.9963999,0.0002956424,0.002556889,0.00008543897,0.000012412526,0.000009624113,0.000012357251,0.000008352504,0.0006193749],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979001,0.0013833707,0.000044099736,0.00027429548,0.00022878237,0.00016928128],"domain_scores_gemma":[0.99451786,0.003782892,0.0003687138,0.00058791816,0.0004676445,0.0002748954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027239826,0.00023497244,0.0002877786,0.0012592413,0.0010254923,0.004286628,0.000892918,0.0016220039,0.002873498],"category_scores_gemma":[0.010142995,0.00029710436,0.00033352987,0.0010570408,0.009332637,0.0042896336,0.0016091259,0.001283385,0.00015792431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008974386,0.000119724165,0.015448687,0.00017290961,0.00017205163,0.0004453127,0.01747528,0.01199782,0.0026692986,0.90035343,0.001074701,0.04998117],"study_design_scores_gemma":[0.000044484124,0.000063381776,0.031103482,0.00008579136,0.00006345011,0.00039222697,0.008705156,0.014053523,0.0009507127,0.93189454,0.012579501,0.000063751264],"about_ca_topic_score_codex":0.006076016,"about_ca_topic_score_gemma":0.005419127,"teacher_disagreement_score":0.006076016,"about_ca_system_score_codex":0.0025190988,"about_ca_system_score_gemma":0.0014472075,"threshold_uncertainty_score":0.018277466},"labels":[],"label_agreement":null},{"id":"W2026301027","doi":"10.1016/j.cogsys.2008.08.003","title":"On the cognitive process of human problem solving","year":2008,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":375,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cognitive model; Cognition; Process (computing); Cognitive computing; Representation (politics); Inference; Set (abstract data type); Abstraction; Object (grammar); Knowledge representation and reasoning; Artificial intelligence; Theoretical computer science; Cognitive science; Psychology; Programming language","score_opus":0.15041819445748544,"score_gpt":0.3934490900271573,"score_spread":0.24303089556967183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026301027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35331064,0.008274736,0.39744258,0.015005226,0.0004095256,0.00030576988,0.00029175053,0.00024250336,0.22471727],"genre_scores_gemma":[0.95681614,0.0015296622,0.037134722,0.00041547368,0.00011698204,0.000105106614,0.00011187805,0.000048276637,0.0037217382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968863,0.0016803134,0.00012687765,0.00039709342,0.00069870945,0.00021055568],"domain_scores_gemma":[0.97078395,0.02551472,0.0009304227,0.0012358514,0.0011096017,0.00042550106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039985613,0.00047763434,0.0004643436,0.0011020792,0.00073678687,0.004371865,0.0011989048,0.0016041786,0.006850049],"category_scores_gemma":[0.036073253,0.0004309634,0.0008198145,0.0010800996,0.004936369,0.007812898,0.0015408077,0.0018587213,0.00049912167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029592979,0.00018889106,0.004142528,0.00039815743,0.00011462703,0.00017620526,0.00654642,0.015671743,0.0022524984,0.8949292,0.0023617207,0.072922036],"study_design_scores_gemma":[0.00005082926,0.00005155413,0.0035394034,0.000056546505,0.00002932878,0.00010043465,0.0006609418,0.02701896,0.00064237026,0.9632032,0.0046194494,0.000026865902],"about_ca_topic_score_codex":0.005956205,"about_ca_topic_score_gemma":0.0020978635,"teacher_disagreement_score":0.006850049,"about_ca_system_score_codex":0.0012557374,"about_ca_system_score_gemma":0.001536932,"threshold_uncertainty_score":0.022915661},"labels":[],"label_agreement":null},{"id":"W2034356075","doi":"10.1016/s1389-0417(02)00056-6","title":"From action to discourse: The bridging function of gestures","year":2002,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Hearing Impairment and Communication","field":"Psychology","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Gesture; Embodied cognition; Meaning (existential); Action (physics); Cognition; Bridging (networking); Function (biology); Modality (human–computer interaction); Cognitive science; Communication; Computer science; Linguistics; Psychology; Human–computer interaction; Artificial intelligence; Neuroscience; Biology","score_opus":0.36490034978154784,"score_gpt":0.5090149342556423,"score_spread":0.14411458447409442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034356075","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":[0.29679644,0.011036692,0.29313335,0.009157264,0.0008847611,0.00015588755,0.0005775411,0.0009033199,0.38735476],"genre_scores_gemma":[0.9529341,0.0021409527,0.03730101,0.00034345637,0.00013898229,0.00009628417,0.00020764305,0.00024734202,0.006590361],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99834025,0.0008106549,0.00007027549,0.00037959937,0.000255125,0.00014409641],"domain_scores_gemma":[0.9945444,0.0039025529,0.00034707872,0.00069231255,0.0002919801,0.00022176564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016781794,0.00084125926,0.00052657945,0.0018570591,0.0013141574,0.009013722,0.0012776775,0.0022366066,0.010244163],"category_scores_gemma":[0.015726322,0.0006502235,0.0007123153,0.0012978239,0.008359055,0.017985418,0.0045958417,0.0021332074,0.0010053109],"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":[0.00046975297,0.000060831902,0.0036524248,0.0007622992,0.000062830586,0.0008594941,0.078217275,0.0015585083,0.02010981,0.7029235,0.0026519133,0.18867135],"study_design_scores_gemma":[0.00008557067,0.0002425736,0.008413929,0.00070827716,0.00011906853,0.0012651731,0.02641288,0.007904064,0.009999179,0.90541464,0.039316896,0.000117741496],"about_ca_topic_score_codex":0.0018319321,"about_ca_topic_score_gemma":0.00083670195,"teacher_disagreement_score":0.010244163,"about_ca_system_score_codex":0.00066502887,"about_ca_system_score_gemma":0.0011026248,"threshold_uncertainty_score":0.034270108},"labels":[],"label_agreement":null},{"id":"W2051229964","doi":"10.1016/j.cogsys.2011.07.001","title":"Who is in charge of science: Men view “Time” as more fixed, “Reality” as less real, and “Order” as less ordered","year":2011,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Science Education and Pedagogy","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Attribution; Natural (archaeology); Meaning (existential); Order (exchange); Natural science; Psychology; Causality (physics); Epistemology; Social psychology; Geography; Philosophy; Physics; Quantum mechanics","score_opus":0.3927318564517747,"score_gpt":0.538631057675624,"score_spread":0.1458992012238493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051229964","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":[0.17253844,0.0065350872,0.027765056,0.34822592,0.0045631747,0.00004589631,0.00017407912,0.00024424354,0.43990812],"genre_scores_gemma":[0.9553627,0.0012434393,0.0021437556,0.021372754,0.00065025757,0.00002047564,0.000038724833,0.00009131047,0.019076616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9950956,0.002444981,0.0001568323,0.00067759724,0.00090646086,0.0007186037],"domain_scores_gemma":[0.98922926,0.0040091164,0.0015183432,0.0007165118,0.0022447458,0.0022821329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008687209,0.00038236188,0.0007180674,0.00210153,0.009976809,0.010260585,0.00091499946,0.0034703556,0.0065933843],"category_scores_gemma":[0.011888323,0.00047852026,0.00043359905,0.0018188709,0.0390474,0.018639723,0.0028854434,0.006989477,0.0014556582],"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":[0.00010769544,0.00009244223,0.014337956,0.00012364346,0.000086285116,0.000333145,0.21915963,0.00019558323,0.001079185,0.7110182,0.0250244,0.028441817],"study_design_scores_gemma":[0.000086031774,0.00010590518,0.007859394,0.0003366639,0.00012740234,0.00068538723,0.13928255,0.0005916106,0.001710875,0.57315046,0.2759235,0.00014018935],"about_ca_topic_score_codex":0.01441903,"about_ca_topic_score_gemma":0.013415904,"teacher_disagreement_score":0.01441903,"about_ca_system_score_codex":0.0031962802,"about_ca_system_score_gemma":0.0047688065,"threshold_uncertainty_score":0.045942903},"labels":[],"label_agreement":null},{"id":"W2069804725","doi":"10.1016/j.cogsys.2010.12.013","title":"Finding MAPs using strongly equivalent high order recurrent symmetric connectionist networks","year":2011,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Maximum a posteriori estimation; A priori and a posteriori; Computer science; Bayesian network; Probabilistic logic; Connectionism; Formalism (music); Theoretical computer science; Artificial intelligence; Representation (politics); Algorithm; Artificial neural network; Mathematics; Maximum likelihood","score_opus":0.3383986585951988,"score_gpt":0.39656228411092104,"score_spread":0.05816362551572224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069804725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15272279,0.00023007314,0.84387326,0.00033745187,0.000028162507,0.00006170399,0.0001549127,0.00035598234,0.0022356382],"genre_scores_gemma":[0.8808426,0.00020769473,0.115465105,0.00008124086,0.000055101216,0.00009273344,0.0003222335,0.000083354025,0.002849877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916244,0.00032605766,0.000047265818,0.00022566944,0.0001509529,0.000087678665],"domain_scores_gemma":[0.99176604,0.0064362083,0.00060812087,0.00052579335,0.0004446346,0.00021910606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012950863,0.00085024844,0.001210706,0.0014019301,0.00062433165,0.0014499251,0.0018950826,0.0018532807,0.0029903925],"category_scores_gemma":[0.013713282,0.0010541264,0.00097190205,0.00078559335,0.0012103058,0.004130387,0.0015657694,0.0014849472,0.0003348872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005668882,0.00027390596,0.003788036,0.0003470011,0.00034313003,0.00045591537,0.00039232147,0.7209157,0.006417333,0.15915737,0.0020707147,0.105271734],"study_design_scores_gemma":[0.000019186818,0.000025830483,0.00022184064,0.0000056293056,0.000015083666,0.000032612024,0.000015742526,0.88339967,0.00054555153,0.115581684,0.00012797087,0.000009182008],"about_ca_topic_score_codex":0.002356587,"about_ca_topic_score_gemma":0.003170866,"teacher_disagreement_score":0.0029903925,"about_ca_system_score_codex":0.00073916605,"about_ca_system_score_gemma":0.0006767361,"threshold_uncertainty_score":0.010003865},"labels":[],"label_agreement":null},{"id":"W2072011736","doi":"10.1016/j.cogsys.2008.09.006","title":"A computational model of visual analogies in design","year":2009,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Design Education and Practice","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Analogy; Computer science; Visual reasoning; Analogical reasoning; Communication design; Visual Basic; Artificial intelligence; Human–computer interaction; Cognitive science; Programming language; Psychology; Multimedia; Epistemology","score_opus":0.25657465488741293,"score_gpt":0.4566101486222191,"score_spread":0.20003549373480617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072011736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057292502,0.00075395696,0.8560176,0.0030642054,0.00012579317,0.000103838625,0.00029417546,0.00044694054,0.08190094],"genre_scores_gemma":[0.81672335,0.00052713486,0.17338444,0.00031397826,0.000077002296,0.00016310251,0.00030493332,0.000074365686,0.008431668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927396,0.0002879197,0.00003883789,0.000167442,0.00017695944,0.000054787335],"domain_scores_gemma":[0.99675924,0.0023072413,0.00018384084,0.00039065635,0.0002510121,0.00010808321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008294114,0.0004962192,0.00042398518,0.0014790299,0.00080581865,0.0039901645,0.0021931431,0.0016270416,0.016481457],"category_scores_gemma":[0.008685986,0.0005644985,0.0014515953,0.0012807784,0.0029728482,0.0075932327,0.0014937274,0.0015209364,0.0010255367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027002681,0.000038267517,0.00038124018,0.00007966263,0.00002157853,0.000096185446,0.0005490503,0.022041358,0.00057697983,0.9576535,0.0006704368,0.017864764],"study_design_scores_gemma":[0.00001918512,0.000023473573,0.00027661675,0.000034436755,0.000015625516,0.0001287745,0.00013530026,0.12433552,0.00030075843,0.8715123,0.0032055425,0.000012544634],"about_ca_topic_score_codex":0.0051814136,"about_ca_topic_score_gemma":0.002856006,"teacher_disagreement_score":0.016481457,"about_ca_system_score_codex":0.0013724575,"about_ca_system_score_gemma":0.0010980724,"threshold_uncertainty_score":0.055135965},"labels":[],"label_agreement":null},{"id":"W2087970185","doi":"10.1016/j.cogsys.2012.12.007","title":"Cognitive modelling of early music reading skill acquisition for piano: A comparison of the Middle-C and Intervallic methods","year":2013,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"Reading (process); Computer science; Piano; Cognition; Focus (optics); Cognitive model; Cognitive psychology; Human–computer interaction; Psychology; Linguistics","score_opus":0.5161311792142582,"score_gpt":0.4801703239969448,"score_spread":0.0359608552173134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087970185","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":[0.72679985,0.00021500523,0.26383427,0.00019238351,0.000019020372,0.00007781139,0.00021304865,0.0002903965,0.008358184],"genre_scores_gemma":[0.9671656,0.000099658646,0.03120246,0.000018672037,0.0000065926993,0.00007506834,0.00018694,0.000054887343,0.0011901824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995017,0.00024260604,0.00002224696,0.00010646854,0.0000790995,0.000047837293],"domain_scores_gemma":[0.9886135,0.00957619,0.00045863359,0.00058344065,0.00051570206,0.00025249866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017957243,0.0004016328,0.00038058576,0.0008821931,0.00024182323,0.0018321396,0.0012215967,0.00048882933,0.005009835],"category_scores_gemma":[0.014134469,0.00020417276,0.0006794572,0.00045518656,0.00040755194,0.0018031131,0.0007623688,0.000789466,0.00039034328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004071384,0.0014060134,0.10585313,0.0005395506,0.00057424564,0.0003752715,0.00628206,0.28986943,0.015846122,0.104129255,0.0019728264,0.46908078],"study_design_scores_gemma":[0.00004724409,0.00027817648,0.040203158,0.000041299798,0.00005858632,0.0000937979,0.00064220384,0.9317819,0.0019207665,0.024175022,0.00070667826,0.000051065897],"about_ca_topic_score_codex":0.019960012,"about_ca_topic_score_gemma":0.0128449,"teacher_disagreement_score":0.019960012,"about_ca_system_score_codex":0.0010859651,"about_ca_system_score_gemma":0.0013294916,"threshold_uncertainty_score":0.039687693},"labels":[],"label_agreement":null},{"id":"W2101110010","doi":"10.1016/j.cogsys.2006.02.001","title":"Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning","year":2006,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Philippe Pinel de Montréal; Université du Québec en Outaouais; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Connectionism; Unsupervised learning; Competitive learning; Artificial intelligence; Computer science; Odds; Machine learning; Associative property; Associative learning; Cognition; Artificial neural network; Benchmark (surveying); Psychology; Cognitive psychology","score_opus":0.04337926785097943,"score_gpt":0.3089418547753327,"score_spread":0.26556258692435325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101110010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19493376,0.0018477052,0.77088684,0.021758992,0.00015969305,0.000049824044,0.0005303841,0.00031320367,0.009519475],"genre_scores_gemma":[0.9515778,0.0016185314,0.04331932,0.0012610245,0.00037179425,0.000062646286,0.00018958449,0.0001226405,0.001476596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99448264,0.0031779478,0.00027022857,0.0011072739,0.00067369704,0.00028827201],"domain_scores_gemma":[0.7835042,0.1895315,0.009074532,0.013065806,0.0036757484,0.0011482519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015599902,0.00094661734,0.0017132595,0.0014402411,0.0009299767,0.0032819957,0.0026066925,0.003152711,0.0030615095],"category_scores_gemma":[0.12421317,0.0016644566,0.0011023527,0.0013474373,0.0077591776,0.018812161,0.0028219458,0.005947837,0.00033231205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004079313,0.00012516342,0.019070067,0.0004325759,0.0008801272,0.00018663335,0.0007495103,0.313933,0.001939417,0.5996069,0.0025563624,0.06011242],"study_design_scores_gemma":[0.000023093786,0.000020948091,0.0033405817,0.000037767782,0.000058602873,0.000040037463,0.00005802716,0.21006352,0.00064200663,0.78511363,0.00057095755,0.000030803538],"about_ca_topic_score_codex":0.004835712,"about_ca_topic_score_gemma":0.0080953995,"teacher_disagreement_score":0.015599902,"about_ca_system_score_codex":0.0019416885,"about_ca_system_score_gemma":0.0012652092,"threshold_uncertainty_score":0.08250117},"labels":[],"label_agreement":null},{"id":"W2121845153","doi":"10.1016/j.cogsys.2007.11.001","title":"Neural affective decision theory: Choices, brains, and emotions","year":2008,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":95,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Counterfactual thinking; Valuation (finance); Psychology; Affect (linguistics); Framing effect; Cognitive psychology; Neuroeconomics; Pleasure; Prospect theory; Loss aversion; Cognition; Framing (construction); Social psychology; Neuroscience; Microeconomics; Economics","score_opus":0.39369871527130534,"score_gpt":0.4888481281584921,"score_spread":0.09514941288718676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121845153","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":[0.7644047,0.022144314,0.09806059,0.0149036385,0.00035218184,0.00006395129,0.00026011016,0.000056876786,0.099753596],"genre_scores_gemma":[0.9873713,0.0028599156,0.0075194007,0.00040083373,0.00010174068,0.000032903343,0.00005359067,0.000012638687,0.0016477307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963856,0.0001626071,0.000010890049,0.00006723901,0.000091227106,0.000029463119],"domain_scores_gemma":[0.9981121,0.001400525,0.00013743751,0.000093389295,0.00012134244,0.00013518846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012246438,0.00029366947,0.00036022978,0.00048423317,0.0003803509,0.0033039562,0.00047550406,0.0008608145,0.0037620917],"category_scores_gemma":[0.005261277,0.00017456847,0.00024052795,0.0007389431,0.0023477615,0.0025411523,0.0005455904,0.0012205046,0.00022287872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007004904,0.0004617011,0.02923195,0.000585677,0.00021397171,0.00025644066,0.003343123,0.0053419466,0.012082815,0.7591598,0.0033800686,0.185242],"study_design_scores_gemma":[0.000056503854,0.00007877372,0.03537987,0.00009499569,0.000051803596,0.00022980967,0.00092660915,0.008094667,0.0013370081,0.9485566,0.005161766,0.000031635],"about_ca_topic_score_codex":0.0008097443,"about_ca_topic_score_gemma":0.0006427486,"teacher_disagreement_score":0.0037620917,"about_ca_system_score_codex":0.00076576136,"about_ca_system_score_gemma":0.00036311935,"threshold_uncertainty_score":0.012585461},"labels":[],"label_agreement":null},{"id":"W2167613311","doi":"10.1016/j.cogsys.2016.04.002","title":"Neural implementation of probabilistic models of cognition","year":2016,"lang":"en","type":"preprint","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Bayes' theorem; Computer science; Artificial intelligence; Probabilistic logic; Artificial neural network; Bayesian probability; Machine learning; Bayesian inference; Inference; Matching (statistics); Bayesian network; Mathematics; Statistics","score_opus":0.1968756832622374,"score_gpt":0.43981464435565,"score_spread":0.2429389610934126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167613311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11309663,0.0007069553,0.8570538,0.0037493196,0.00027304806,0.000026672633,0.00034256163,0.00047194446,0.024279013],"genre_scores_gemma":[0.9598893,0.00041570453,0.035416048,0.00014786019,0.000088562134,0.000034229033,0.00013618675,0.000040951712,0.0038312024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964094,0.00013143389,0.000017888777,0.000076850054,0.000090788584,0.0000420731],"domain_scores_gemma":[0.998697,0.00071568234,0.00013287028,0.00018830255,0.00018479416,0.000081414066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082298374,0.00029504966,0.00037847876,0.00038181024,0.0002467014,0.0016575586,0.0012837241,0.0010332647,0.0043357033],"category_scores_gemma":[0.006904865,0.00038507432,0.00051948876,0.0004175832,0.00066658284,0.0029502092,0.00080461515,0.0013633475,0.00035932395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011317827,0.000071013776,0.0014709404,0.00012679778,0.00011900217,0.00008500712,0.00018623727,0.23915863,0.0058020814,0.68164337,0.002365868,0.06885789],"study_design_scores_gemma":[0.000008338631,0.0000132400455,0.000588895,0.000008957536,0.000013106726,0.000033569064,0.000017634085,0.57986224,0.00053655717,0.41824362,0.0006625512,0.000011203958],"about_ca_topic_score_codex":0.002123059,"about_ca_topic_score_gemma":0.0021670195,"teacher_disagreement_score":0.0043357033,"about_ca_system_score_codex":0.0006554786,"about_ca_system_score_gemma":0.0005693709,"threshold_uncertainty_score":0.014504373},"labels":[],"label_agreement":null},{"id":"W2293913408","doi":"10.1016/j.cogsys.2015.12.001","title":"Human stigmergy: Theoretical developments and new applications","year":2015,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Psychological Well-being and Life Satisfaction","field":"Psychology","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Stigmergy; Computer science; Human–computer interaction; Cognitive science; Artificial intelligence; Psychology","score_opus":0.34278372576864385,"score_gpt":0.5148369273721971,"score_spread":0.17205320160355325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293913408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1599998,0.12846437,0.45018247,0.03906316,0.0010656471,0.00008610407,0.0003361763,0.0002577617,0.22054453],"genre_scores_gemma":[0.9516355,0.021173963,0.022466037,0.0005727255,0.00048427863,0.0000656024,0.000053431,0.00003698016,0.003511474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994936,0.00024711643,0.000027038965,0.000108837936,0.000075753145,0.000047613248],"domain_scores_gemma":[0.99537694,0.0036953753,0.00025927392,0.0003078434,0.00020370758,0.00015676866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018969136,0.0007369354,0.0008798481,0.001987779,0.00074252096,0.0054378873,0.0010233481,0.0016961108,0.0070276433],"category_scores_gemma":[0.005397492,0.00038601118,0.0007166666,0.0027767299,0.008026161,0.0067131603,0.0016419048,0.0022414215,0.00046615946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012434478,0.000020151972,0.0008668198,0.00014402233,0.000020881402,0.00002442902,0.0004020835,0.0036434315,0.00010317853,0.9819973,0.00049919885,0.012265984],"study_design_scores_gemma":[0.0000052612513,0.000010728388,0.0006428748,0.00004149843,0.00001130581,0.00004295683,0.0003016095,0.005607262,0.000054470056,0.98961186,0.003661955,0.00000832121],"about_ca_topic_score_codex":0.0014474192,"about_ca_topic_score_gemma":0.0013206066,"teacher_disagreement_score":0.0070276433,"about_ca_system_score_codex":0.0022071714,"about_ca_system_score_gemma":0.0010276022,"threshold_uncertainty_score":0.02350986},"labels":[],"label_agreement":null},{"id":"W2461724298","doi":"10.1016/j.cogsys.2016.07.002","title":"Specifying and testing the design rationale of social robots for behavior change in children","year":2016,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Situated; Computer science; Process (computing); Human–computer interaction; Engineering design process; Robot; Iterative and incremental development; Artificial intelligence; Knowledge management; Software engineering; Engineering; Programming language","score_opus":0.6309742702964204,"score_gpt":0.5495705378653094,"score_spread":0.08140373243111099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461724298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3434848,0.00022103848,0.636951,0.0017499261,0.00010726361,0.003678407,0.00020943813,0.0027099496,0.010888237],"genre_scores_gemma":[0.6027542,0.00008760674,0.39365503,0.00020216599,0.000009778175,0.0016418566,0.00009949702,0.00017891146,0.0013709457],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.987806,0.008798061,0.00077183667,0.0008372569,0.0013406181,0.00044606286],"domain_scores_gemma":[0.9288124,0.05881812,0.0035439779,0.0041774637,0.0040964177,0.0005515956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013999835,0.0010090206,0.00030592325,0.00067941414,0.0005985042,0.0029111248,0.0017120945,0.0018061581,0.0035614937],"category_scores_gemma":[0.06661812,0.000767496,0.0007770883,0.0001792348,0.0023567462,0.002222767,0.0013304404,0.0015013982,0.00037938674],"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":[0.0038516927,0.0046075075,0.052485984,0.0065789144,0.0006219522,0.0012744087,0.025087187,0.22334109,0.056688383,0.27171543,0.0048565255,0.34889093],"study_design_scores_gemma":[0.0029884926,0.007226826,0.013683389,0.0021353974,0.0013561172,0.0008940964,0.007690826,0.70085406,0.11369001,0.09538338,0.053770814,0.00032657312],"about_ca_topic_score_codex":0.0017001873,"about_ca_topic_score_gemma":0.0028332367,"teacher_disagreement_score":0.013999835,"about_ca_system_score_codex":0.001614504,"about_ca_system_score_gemma":0.0034018382,"threshold_uncertainty_score":0.0740391},"labels":[],"label_agreement":null},{"id":"W2904290041","doi":"10.1016/j.cogsys.2018.12.001","title":"A novel machine learning approach for early detection of hepatocellular carcinoma patients","year":2018,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":126,"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":"Feature selection; Support vector machine; Hepatocellular carcinoma; Preprocessor; Normalization (sociology); Artificial intelligence; Machine learning; Computer science; Liver cancer; Data pre-processing; Selection (genetic algorithm); Feature (linguistics); Genetic algorithm; Medicine; Internal medicine","score_opus":0.3858015286476613,"score_gpt":0.5059562624752874,"score_spread":0.12015473382762604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904290041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25729132,0.0023341011,0.7274315,0.001974197,0.00035901368,0.00044470953,0.0008058093,0.0019199444,0.0074395183],"genre_scores_gemma":[0.8704461,0.0005053374,0.12362872,0.00025127231,0.00014336713,0.00025989118,0.0006902731,0.000023715536,0.004051329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958307,0.00009546312,0.000033221782,0.00012182888,0.00011238816,0.000053985863],"domain_scores_gemma":[0.9996171,0.00012980418,0.00003571137,0.000016685626,0.00018092731,0.000019684207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070032256,0.000473021,0.00062453037,0.0011623772,0.00033943183,0.0006560864,0.00079511973,0.0007153538,0.001093881],"category_scores_gemma":[0.0013712327,0.00015520997,0.0006542653,0.0006856276,0.00016684309,0.0005868585,0.00036890162,0.0005957743,0.00047563165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004290772,0.0009281649,0.053863265,0.0002000122,0.00032039086,0.0005443064,0.00019964822,0.14181085,0.011266461,0.003711198,0.008189039,0.7785377],"study_design_scores_gemma":[0.00001810035,0.0001596704,0.0076062735,0.000019533145,0.00006148784,0.00022702181,0.000035332534,0.9862099,0.0024136452,0.0015266982,0.0017028552,0.000019498695],"about_ca_topic_score_codex":0.004420785,"about_ca_topic_score_gemma":0.0037203864,"teacher_disagreement_score":0.004420785,"about_ca_system_score_codex":0.00059184403,"about_ca_system_score_gemma":0.00091042253,"threshold_uncertainty_score":0.008790135},"labels":[],"label_agreement":null},{"id":"W2976946980","doi":"10.1016/j.cogsys.2019.09.016","title":"Empathy framework for embodied conversational agents","year":2019,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Empathy; Embodied cognition; Dialog system; Psychology; Cognition; Modal; Cognitive psychology; Computer science; Human–computer interaction; Social psychology; Artificial intelligence; Dialog box","score_opus":0.29671552636516557,"score_gpt":0.5400133516552651,"score_spread":0.24329782529009952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976946980","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013516545,0.00078542135,0.9297595,0.0021929664,0.00014291708,0.000092251685,0.00008881239,0.00016750055,0.053254135],"genre_scores_gemma":[0.7206709,0.00065941864,0.25435728,0.00047441726,0.00019305987,0.00033613303,0.00013110747,0.00008699144,0.023090735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99896777,0.0004993524,0.000048717466,0.00016864098,0.00016252216,0.00015303624],"domain_scores_gemma":[0.99926394,0.00034793082,0.00008337512,0.00009245152,0.00012601881,0.00008630983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011744217,0.00063721946,0.0004968387,0.0006350384,0.0011717114,0.0019943966,0.001284477,0.0016427821,0.008346633],"category_scores_gemma":[0.0028945259,0.00034389817,0.00091065065,0.0003846832,0.001975365,0.003021583,0.003009479,0.0019752572,0.0009429218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012261551,0.000019494559,0.00012475964,0.000052380175,0.000014646298,0.00010903055,0.0009443095,0.0043476657,0.0014258288,0.9841472,0.00073633535,0.008066081],"study_design_scores_gemma":[0.00001867477,0.000041778247,0.0004910179,0.000057288027,0.000032526576,0.00017645954,0.00069832377,0.11694523,0.0008626495,0.8657936,0.014856628,0.000025865063],"about_ca_topic_score_codex":0.0018436515,"about_ca_topic_score_gemma":0.0014518151,"teacher_disagreement_score":0.008346633,"about_ca_system_score_codex":0.00096592837,"about_ca_system_score_gemma":0.0009425396,"threshold_uncertainty_score":0.027922332},"labels":[],"label_agreement":null},{"id":"W300365311","doi":"10.1016/j.cogsys.2007.10.001","title":"","year":2007,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Psychology","score_opus":0.30583758069482986,"score_gpt":0.524577969134824,"score_spread":0.21874038843999416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W300365311","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006759832,0.0023615698,0.0070790094,0.011684037,0.0010366444,0.000020360101,0.00045351652,0.0002600596,0.97034484],"genre_scores_gemma":[0.25890556,0.0054969424,0.0051985523,0.0021447127,0.0006169211,0.00005065958,0.0014610662,0.00022104078,0.7259046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997329,0.00004739828,0.00000999126,0.000060310544,0.00012119445,0.000028186307],"domain_scores_gemma":[0.99861324,0.00033834454,0.000045161465,0.0003622707,0.00047628352,0.00016467077],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00052970817,0.0003466452,0.00029412255,0.0009834152,0.0013026245,0.004531539,0.00047159672,0.0009363752,0.24176218],"category_scores_gemma":[0.0030962713,0.00021479945,0.0002307321,0.0010484162,0.0018390452,0.0037667034,0.00090629794,0.001155745,0.06280658],"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":[0.000095490075,0.00010574809,0.002384963,0.00017121187,0.000021016313,0.000077762554,0.0012273096,0.0001630162,0.0011906167,0.46672696,0.27642697,0.25140887],"study_design_scores_gemma":[0.000020716308,0.000030899548,0.006144529,0.00017998272,0.00003884464,0.00031575962,0.0012920292,0.0004789982,0.0015157489,0.31884903,0.6711196,0.000013926042],"about_ca_topic_score_codex":0.0026186423,"about_ca_topic_score_gemma":0.0027427594,"teacher_disagreement_score":0.75823784,"about_ca_system_score_codex":0.0011370118,"about_ca_system_score_gemma":0.0012169066,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3209504261","doi":"10.1016/j.cogsys.2021.10.002","title":"ECNN: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder","year":2021,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convolutional neural network; Autism; Computer science; Autism spectrum disorder; Artificial intelligence; Pattern recognition (psychology); Brain function; Neuroscience; Psychology","score_opus":0.08565360583153797,"score_gpt":0.3798781183226793,"score_spread":0.29422451249114134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209504261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2740012,0.004886678,0.69040996,0.0013489462,0.0006812729,0.0002157108,0.0063755666,0.011975594,0.010105058],"genre_scores_gemma":[0.7486914,0.0008784794,0.22931394,0.00044152298,0.00011515333,0.00011840509,0.0057715834,0.000225567,0.014443936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981934,0.00002677481,0.000010678679,0.00005275967,0.00004984412,0.000040606],"domain_scores_gemma":[0.9997316,0.00007390323,0.000018028497,0.000031828513,0.00012666047,0.000017995071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045298575,0.00065111555,0.0002871129,0.0007057919,0.0002570692,0.00030965754,0.00058952085,0.0007305973,0.0023064767],"category_scores_gemma":[0.0010637414,0.00020153192,0.00035820814,0.00033873084,0.00011181418,0.00040247603,0.0006173851,0.0005415794,0.0008039415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006254474,0.00022483792,0.01584488,0.00018200674,0.00019496217,0.0007361775,0.00006501371,0.08070099,0.05008824,0.002507159,0.030834962,0.8179954],"study_design_scores_gemma":[0.000024069122,0.00008178636,0.008539383,0.000036011937,0.00007019564,0.0006015811,0.000025724523,0.9542557,0.029549697,0.0017061265,0.0050873244,0.00002249552],"about_ca_topic_score_codex":0.019110521,"about_ca_topic_score_gemma":0.029826632,"teacher_disagreement_score":0.019110521,"about_ca_system_score_codex":0.00046214057,"about_ca_system_score_gemma":0.00075085653,"threshold_uncertainty_score":0.037998617},"labels":[],"label_agreement":null},{"id":"W4321378413","doi":"10.1016/j.cogsys.2023.02.004","title":"Information trust model","year":2023,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Access Control and Trust","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Normative; Computer science; Normative model of decision-making; Construct (python library); Perception; Trustworthiness; Social media; Knowledge management; Cognition; Value (mathematics); Internet privacy; Data science; Psychology; World Wide Web; Political science","score_opus":0.21068627314023616,"score_gpt":0.4783608063091872,"score_spread":0.26767453316895107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321378413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099024266,0.0013832336,0.55310357,0.0149990795,0.00038452313,0.00031679458,0.0011501646,0.0005183269,0.32912004],"genre_scores_gemma":[0.92297566,0.00072779955,0.016646555,0.0004286103,0.00018144997,0.00016412146,0.00024481912,0.00003964867,0.058591433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99799585,0.00078716665,0.00009241163,0.0003233623,0.00055357267,0.0002476476],"domain_scores_gemma":[0.99237955,0.0038846205,0.0006904183,0.0014029195,0.0012521812,0.00039030478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019976262,0.0006321664,0.0006541082,0.0015639896,0.0011002184,0.0038805688,0.0014359457,0.0026445275,0.014916828],"category_scores_gemma":[0.016565353,0.0004024133,0.0006687928,0.0017755098,0.002238965,0.008093596,0.0012497993,0.0020666176,0.0031098241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045783923,0.000046173078,0.00066843326,0.000053069012,0.000036527512,0.00013499343,0.00030085206,0.012279372,0.00028095176,0.96918577,0.004516152,0.0124520045],"study_design_scores_gemma":[0.000051321796,0.000068435686,0.00076915004,0.000043351447,0.0000673887,0.0003581587,0.00020209295,0.14637393,0.0004207663,0.83739537,0.0142156,0.000034426394],"about_ca_topic_score_codex":0.0051422184,"about_ca_topic_score_gemma":0.0024851959,"teacher_disagreement_score":0.014916828,"about_ca_system_score_codex":0.0018013511,"about_ca_system_score_gemma":0.0012617757,"threshold_uncertainty_score":0.049901783},"labels":[],"label_agreement":null},{"id":"W4323262652","doi":"10.1016/j.cogsys.2023.02.009","title":"Computing word meanings by aggregating individualized distributional models: Wisdom of the crowds in lexical semantic memory","year":2023,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity (geometry); Judgement; Semantic similarity; Word (group theory); Computer science; Semantic memory; Natural language processing; Aggregate (composite); Artificial intelligence; Psychology; Cognitive psychology; Cognition; Linguistics","score_opus":0.13329042708033806,"score_gpt":0.3754494462827638,"score_spread":0.24215901920242572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323262652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31490126,0.002582781,0.67364734,0.0047159996,0.00028283425,0.00010054866,0.00036799975,0.00044101072,0.0029601525],"genre_scores_gemma":[0.91786754,0.0007705017,0.07903577,0.00037419266,0.0004158864,0.000102643346,0.00040711573,0.0001364333,0.0008898452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99477094,0.002961324,0.00040885602,0.0011179486,0.00053841865,0.00020256429],"domain_scores_gemma":[0.92199343,0.06722436,0.002130692,0.0055495156,0.0021321413,0.000969867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01496651,0.0011605434,0.00314056,0.0040307087,0.001726616,0.006398574,0.0027147764,0.0032501733,0.0019094561],"category_scores_gemma":[0.1003358,0.0011919627,0.001658943,0.0042202906,0.0030648324,0.017423047,0.0044049746,0.004116752,0.000491754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011774172,0.00068516197,0.039602056,0.000632804,0.0014406996,0.00045342618,0.006133634,0.3699652,0.0020524957,0.18611951,0.009301227,0.38243636],"study_design_scores_gemma":[0.000032927466,0.000035866273,0.0015523861,0.000032202854,0.000066799395,0.000046700206,0.00042295025,0.51246053,0.0002967888,0.48443192,0.0005816709,0.000039285034],"about_ca_topic_score_codex":0.006718638,"about_ca_topic_score_gemma":0.007835204,"teacher_disagreement_score":0.01496651,"about_ca_system_score_codex":0.001484164,"about_ca_system_score_gemma":0.0014692366,"threshold_uncertainty_score":0.07915139},"labels":[],"label_agreement":null},{"id":"W4389410579","doi":"10.1016/j.cogsys.2023.101196","title":"Designing a wheel-based assessment tool to measure visual aesthetic emotions","year":2023,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feeling; Psychology; Valence (chemistry); Set (abstract data type); Cognitive psychology; Arousal; Emotion classification; Affect (linguistics); Affective science; Human–computer interaction; Computer science; Social psychology; Communication","score_opus":0.22715961413543206,"score_gpt":0.46444391652394634,"score_spread":0.23728430238851428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389410579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11869013,0.0001342804,0.8688577,0.00021367442,0.0001740376,0.001597256,0.00088480324,0.0030732444,0.006374886],"genre_scores_gemma":[0.34596795,0.00018074203,0.6428523,0.0002339908,0.000027222255,0.0024806282,0.00076654763,0.0003876756,0.007102945],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905056,0.00030539787,0.00006492599,0.00012547521,0.0003562711,0.00009745458],"domain_scores_gemma":[0.997031,0.0010536384,0.00016412318,0.00015724736,0.001418848,0.00017508383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020417648,0.0006768058,0.00053346064,0.0018198696,0.00039408955,0.0014700678,0.00095030887,0.0009426128,0.007960481],"category_scores_gemma":[0.0054282094,0.00047486447,0.000616338,0.0007984347,0.0003975046,0.0013613586,0.0013540727,0.00044421453,0.0029306835],"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":[0.001246352,0.00094821444,0.039573204,0.0013438816,0.00014179746,0.0002541411,0.0018393716,0.0056387275,0.35722482,0.008330646,0.013186699,0.5702721],"study_design_scores_gemma":[0.00052507664,0.0038653912,0.20260064,0.0008648246,0.0006586402,0.0021985513,0.004745607,0.33408487,0.34776735,0.018042346,0.08382331,0.000823454],"about_ca_topic_score_codex":0.0023530112,"about_ca_topic_score_gemma":0.0043413555,"teacher_disagreement_score":0.007960481,"about_ca_system_score_codex":0.0005433466,"about_ca_system_score_gemma":0.0010635995,"threshold_uncertainty_score":0.026630402},"labels":[],"label_agreement":null},{"id":"W4407230387","doi":"10.1016/j.cogsys.2025.101334","title":"Combination of reward-modulated spike-timing dependent plasticity and temporal difference long-term potentiation in actor–critic spiking neural network","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Science Foundation","keywords":"Long-term potentiation; Spike-timing-dependent plasticity; Spike (software development); Neuroscience; Spiking neural network; Term (time); Plasticity; Computer science; Neuroplasticity; Synaptic plasticity; Duration (music); Artificial neural network; Psychology; Artificial intelligence; Cognitive psychology; Biology; Physics","score_opus":0.06493077052038645,"score_gpt":0.35284284871533894,"score_spread":0.28791207819495246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407230387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39065957,0.0009432566,0.6004116,0.0009989652,0.00020123977,0.000030057428,0.000074002965,0.0002605329,0.006420739],"genre_scores_gemma":[0.99375325,0.000098764205,0.0052586384,0.000024133005,0.000013954503,0.00000892699,0.000010893459,0.000010643874,0.0008206908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998543,0.0000401904,0.000011504063,0.000040915762,0.000028085746,0.000024842971],"domain_scores_gemma":[0.99938357,0.00034933872,0.00007902539,0.00004689947,0.00008487661,0.00005639235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068564655,0.000380799,0.00059025764,0.00015745983,0.00020497269,0.0006323749,0.0009143357,0.0008201575,0.00079215027],"category_scores_gemma":[0.002380317,0.00032255365,0.00039795105,0.00018920058,0.00044522499,0.0006932585,0.00059444626,0.0008453465,0.000072267845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030144418,0.000106392625,0.0031796948,0.00015433868,0.00026859855,0.00042307633,0.00008339647,0.8747582,0.039141186,0.0470767,0.0007803842,0.03372664],"study_design_scores_gemma":[0.0000034020077,0.000011031423,0.00021592455,0.0000018205919,0.0000098981745,0.000020464304,0.0000014651807,0.99613357,0.00071916665,0.0028363927,0.000043608787,0.0000033385622],"about_ca_topic_score_codex":0.0009955604,"about_ca_topic_score_gemma":0.0017806236,"teacher_disagreement_score":0.0009955604,"about_ca_system_score_codex":0.00036009328,"about_ca_system_score_gemma":0.000394671,"threshold_uncertainty_score":0.0036261082},"labels":[],"label_agreement":null},{"id":"W4407893628","doi":"10.1016/j.cogsys.2025.101338","title":"Neurons as autonomous agents: A biologically inspired framework for cognitive architectures in artificial intelligence","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Autonomous agent; Artificial intelligence; Computer science; Cognition; Intelligent agent; Cognitive science; Human–computer interaction; Neuroscience; Psychology","score_opus":0.1635600864353549,"score_gpt":0.44504370553738937,"score_spread":0.28148361910203445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407893628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009739502,0.052834455,0.86663693,0.009366142,0.0009116896,0.000110044246,0.00015463961,0.0004259402,0.0598207],"genre_scores_gemma":[0.44174057,0.07148645,0.4542428,0.0026890053,0.0013571235,0.0008949374,0.000258405,0.00020949876,0.027121175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996574,0.00012682693,0.000024374933,0.00007082583,0.00008764935,0.000032856413],"domain_scores_gemma":[0.99965966,0.00015709583,0.000051405183,0.000039517752,0.000057026817,0.000035249894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068039866,0.00085966947,0.0006414531,0.0009751154,0.00062216824,0.0029882835,0.0017907874,0.0024885365,0.0022453386],"category_scores_gemma":[0.0010248823,0.00041097723,0.00071204896,0.00083693437,0.004096173,0.0034464153,0.0014796983,0.0025662526,0.00071847433],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012056236,0.000011994354,0.00020958931,0.00029810646,0.000037510912,0.00010146238,0.00022231955,0.022708274,0.0014961878,0.9473168,0.0016442236,0.025941605],"study_design_scores_gemma":[0.000011682625,0.00003693808,0.00017142968,0.00017551394,0.000027967972,0.00012443263,0.00010033134,0.061190818,0.00071173575,0.88037425,0.057049446,0.000025427464],"about_ca_topic_score_codex":0.0022333995,"about_ca_topic_score_gemma":0.002125842,"teacher_disagreement_score":0.0029882835,"about_ca_system_score_codex":0.0012199745,"about_ca_system_score_gemma":0.0010966172,"threshold_uncertainty_score":0.008851588},"labels":[],"label_agreement":null},{"id":"W4410774857","doi":"10.1016/j.cogsys.2025.101372","title":"An integrative model of self-efficacy within a computational cognitive architecture","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Institute for the Behavioral and Social Sciences; Aurora Research Institute","keywords":"Cognitive architecture; Architecture; Cognition; Cognitive science; Computer science; Computational model; Cognitive model; Psychology; Artificial intelligence; Neuroscience; Art","score_opus":0.07130760167635014,"score_gpt":0.4009403967192597,"score_spread":0.32963279504290954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29222012,0.00058558554,0.49515283,0.00765678,0.00008184689,0.00013937597,0.00023149271,0.0002345947,0.20369732],"genre_scores_gemma":[0.9741465,0.00009560784,0.022736678,0.00008041879,0.000017988885,0.00007787702,0.000034447326,0.00001455322,0.0027958911],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993562,0.00030419297,0.000025605688,0.00012978305,0.00011613059,0.00006807098],"domain_scores_gemma":[0.99793756,0.0012396883,0.00014661177,0.00022678956,0.0002805107,0.00016893056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011157012,0.0003837016,0.00033097147,0.00093060866,0.00067896815,0.0024918562,0.0012497534,0.0009637792,0.0058051543],"category_scores_gemma":[0.003870402,0.00028506207,0.00062565756,0.0007571688,0.0025125125,0.0040623094,0.00097180705,0.0009826771,0.0005115072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011172794,0.00004709057,0.0016174623,0.000025814235,0.000025404062,0.000042500364,0.0007700605,0.01213559,0.0003286199,0.9788029,0.0002616391,0.0059317164],"study_design_scores_gemma":[0.000014982609,0.0000629437,0.0030112383,0.000026692202,0.000033080163,0.00007113109,0.0003967966,0.077371016,0.00020682895,0.9165228,0.002268185,0.000014284432],"about_ca_topic_score_codex":0.0020123015,"about_ca_topic_score_gemma":0.0017161327,"teacher_disagreement_score":0.0058051543,"about_ca_system_score_codex":0.0011331349,"about_ca_system_score_gemma":0.0012628555,"threshold_uncertainty_score":0.019420207},"labels":[],"label_agreement":null},{"id":"W4413841290","doi":"10.1016/j.cogsys.2025.101389","title":"Modeling behavioral deviations in ADLs using Inverse Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Elevator Systems and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Reinforcement; Inverse; Artificial intelligence; Psychology; Computer science; Cognitive psychology; Machine learning; Mathematics; Social psychology","score_opus":0.1237326636277065,"score_gpt":0.39660900715389347,"score_spread":0.27287634352618695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413841290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32157338,0.00032263043,0.6737147,0.0006418886,0.000056764115,0.00015078184,0.0003449851,0.0006882205,0.00250653],"genre_scores_gemma":[0.97995263,0.00006588414,0.018444994,0.00006438229,0.000010661672,0.0000965334,0.0001117346,0.000014418173,0.0012387222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995105,0.00014400568,0.000027300346,0.00015074507,0.000095392046,0.0000720206],"domain_scores_gemma":[0.9979685,0.0012423618,0.0003642524,0.00008161828,0.00022282988,0.000120418605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235285,0.0007371265,0.0006514113,0.00053120806,0.00018063119,0.0005692119,0.0009476191,0.0007420803,0.0013226211],"category_scores_gemma":[0.0057845255,0.00033461978,0.00046154714,0.000244938,0.00063823996,0.00056386716,0.0006215889,0.0011022331,0.0001818587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120520795,0.00013335868,0.013257668,0.000035934656,0.000049245315,0.00013020808,0.00006452145,0.9628618,0.0009838366,0.0026585143,0.00037047899,0.01933388],"study_design_scores_gemma":[0.0000048442075,0.000022241571,0.0005588239,0.0000022366846,0.000003342049,0.000008834827,0.0000031225327,0.99828243,0.000085936255,0.0009798737,0.000045814446,0.0000025507761],"about_ca_topic_score_codex":0.012056028,"about_ca_topic_score_gemma":0.010597418,"teacher_disagreement_score":0.012056028,"about_ca_system_score_codex":0.0008370916,"about_ca_system_score_gemma":0.0009152505,"threshold_uncertainty_score":0.023971677},"labels":[],"label_agreement":null},{"id":"W4416828434","doi":"10.1016/j.cogsys.2025.101422","title":"Dual or unified: optimizing drive-based reinforcement learning for cognitive autonomous robots","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Reinforcement Learning in Robotics","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":"Artificial Intelligence in Medicine (Canada)","funders":"Ministério da Ciência, Tecnologia e Inovação; Ministério da Ciência, Tecnologia, Inovações e Comunicações; Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Brazilian Institute of Neuroscience and Neurotechnology","keywords":"Reinforcement learning; Modular design; Dual (grammatical number); Cognition; Curiosity; Selection (genetic algorithm); Robot; Autonomous agent; Action selection","score_opus":0.10680813833711963,"score_gpt":0.39253079557312515,"score_spread":0.28572265723600554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416828434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049399488,0.0003479618,0.94384676,0.0002718427,0.00009982135,0.00007382079,0.000044955606,0.00057729456,0.005337893],"genre_scores_gemma":[0.92093045,0.00010129035,0.07568845,0.00012698976,0.000039293136,0.00012563591,0.00004619018,0.00007309167,0.0028685005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958795,0.00012620293,0.000018905153,0.00008332901,0.00009856253,0.000085065934],"domain_scores_gemma":[0.99898654,0.0004946681,0.00010109703,0.00011549751,0.00019655295,0.00010564488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013914037,0.000893518,0.0012559909,0.00040274812,0.00042159684,0.0009508038,0.001783544,0.0013122595,0.0027569404],"category_scores_gemma":[0.003734657,0.0005540649,0.00048703357,0.0003302522,0.0011619709,0.0012372661,0.0019238959,0.0015036617,0.00034579937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003103782,0.00015868951,0.0006394901,0.000116437914,0.000072709845,0.00007774826,0.00009802503,0.91036445,0.0026544956,0.018704066,0.0016175582,0.065186],"study_design_scores_gemma":[0.000016851172,0.000050370145,0.000044974622,0.0000039850947,0.0000062612958,0.0000061102483,0.0000040026725,0.9943224,0.00026024086,0.005116531,0.00016461877,0.0000036062536],"about_ca_topic_score_codex":0.0038203818,"about_ca_topic_score_gemma":0.0043274257,"teacher_disagreement_score":0.0038203818,"about_ca_system_score_codex":0.00077835814,"about_ca_system_score_gemma":0.0012734636,"threshold_uncertainty_score":0.009222925},"labels":[],"label_agreement":null}]}