{"id":"W3127516156","doi":"10.71781/449","title":"Bayesian modeling of biological motion perception in sport","year":2020,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Winter Sports Injuries and Performance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian probability; Perception; Biological motion; Motion (physics); Artificial intelligence; Computer science; Psychology; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009601668,0.0002289848,0.0004500124,0.0002510297,0.0005439779,0.000007236703,0.0001236253,0.0003726538,0.00007463761],"category_scores_gemma":[0.00001435083,0.0002312031,0.0002209504,0.0002046339,0.00006420493,0.0001344322,0.00003851693,0.0003819198,0.000009051954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546597,"about_ca_system_score_gemma":0.0005300004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005718787,"about_ca_topic_score_gemma":0.0009247594,"domain_scores_codex":[0.9986575,0.00001545461,0.0004260484,0.0003679755,0.000338902,0.0001940681],"domain_scores_gemma":[0.999296,0.000006496081,0.0002218509,0.0001932287,0.0001357811,0.0001466595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02240821,0.001247493,0.7483955,0.002570194,0.0006422353,0.00753692,0.1077551,0.0201404,0.04926646,0.008823634,0.0004694073,0.03074442],"study_design_scores_gemma":[0.003095757,0.0008376012,0.7291085,0.00229456,0.0005196469,0.0009417936,0.06114157,0.1864743,0.005373611,0.0002549072,0.00900811,0.0009496746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728484,0.001627798,0.0004628089,0.000125334,0.0004089065,0.0002824009,0.00001300308,0.00004921777,0.02418209],"genre_scores_gemma":[0.9931219,0.0006662494,0.0005276222,0.00005757752,0.0001512509,0.000007808682,0.001062182,0.00001669603,0.004388677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1663339,"threshold_uncertainty_score":0.9428188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009343869716488198,"score_gpt":0.2012673833554022,"score_spread":0.191923513638914,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}