{"id":"W4313429003","doi":"10.1109/urtc56832.2022.10002186","title":"Using Machine Learning to Predict Injury Risk From Athlete Kinetic Patterns","year":2022,"lang":"en","type":"article","venue":"","topic":"Sports injuries and prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Kinetic energy; Machine learning; Kinetic theory; Artificial intelligence; Physics; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001722886,0.0009057412,0.0006119168,0.00325294,0.0002248758,0.001027893,0.0004755285,0.0006054955,0.001032884],"category_scores_gemma":[0.006453273,0.0002332861,0.0006512313,0.001267282,0.0002422661,0.0007426002,0.0004760866,0.0007799352,0.0005539731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003730511,"about_ca_system_score_gemma":0.0005911407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006274934,"about_ca_topic_score_gemma":0.006849339,"domain_scores_codex":[0.9993231,0.0002398623,0.0000774521,0.0001396877,0.000137844,0.00008207509],"domain_scores_gemma":[0.9974121,0.00177533,0.0003117812,0.0001289733,0.0002865292,0.00008527445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004364124,0.0006417086,0.5015305,0.0001108696,0.0004189926,0.000200045,0.0001260629,0.258949,0.001587077,0.0009975033,0.001347292,0.2336545],"study_design_scores_gemma":[0.00001241599,0.0001833618,0.06199291,0.00004062413,0.00004368603,0.0001089003,0.00008663101,0.933035,0.0006960589,0.003307851,0.0004700629,0.00002252868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8123715,0.001278044,0.1791734,0.0006377403,0.0001108928,0.0001766249,0.001479428,0.0007990089,0.003973453],"genre_scores_gemma":[0.9804925,0.0002240193,0.01747447,0.00004359356,0.00003636159,0.00004597526,0.0009592368,0.00001092506,0.0007129785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006274934,"threshold_uncertainty_score":0.0124768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01806648105302883,"score_gpt":0.2839642464660865,"score_spread":0.2658977654130577,"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."}}