{"id":"W3217600905","doi":"10.1136/bjsports-2021-ioc.391","title":"427 Applying bayesian networks to injury occurrence in professional football","year":2021,"lang":"en","type":"article","venue":"Poster presentations","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Football; Bayesian network; League; Computer science; Bayesian probability; Outcome (game theory); Statistics; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002223756,0.0001060559,0.0001312095,0.0001096672,0.0001388158,0.0001243134,0.000443741,0.00005995491,0.00003324849],"category_scores_gemma":[0.00007100659,0.00009744781,0.00004748374,0.0008395415,0.00002038777,0.0005046566,0.0004177057,0.0001790374,0.00006492062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000363956,"about_ca_system_score_gemma":0.0001611647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003906636,"about_ca_topic_score_gemma":0.00008604417,"domain_scores_codex":[0.9986309,0.000141304,0.0003013031,0.0003933311,0.0002460793,0.0002870377],"domain_scores_gemma":[0.9990543,0.0001465096,0.0000473152,0.0005033344,0.0001329222,0.0001155971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002473899,0.0003896078,0.892378,0.0001167466,0.00002532091,0.00005613077,0.007242581,0.00576822,0.0004136492,0.002240287,0.01775591,0.07358879],"study_design_scores_gemma":[0.001175045,0.0001362117,0.8603154,0.0004722753,0.00001580479,0.00008357566,0.0008458961,0.115886,0.003352405,0.00264273,0.01409369,0.0009809765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1691166,0.000189367,0.8174506,0.005176315,0.004447663,0.001218197,0.00002443388,0.0002302561,0.002146603],"genre_scores_gemma":[0.9928265,0.00000497147,0.005515934,0.0007790647,0.00008147275,0.0003237029,0.00002342492,0.000004840879,0.0004401179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8237098,"threshold_uncertainty_score":0.3973807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705125680514257,"score_gpt":0.3104245964888147,"score_spread":0.2933733396836721,"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."}}