{"id":"W4387578642","doi":"10.1080/24748668.2023.2268480","title":"Bayesian inference of the impulse-response model of athlete training and performance","year":2023,"lang":"en","type":"article","venue":"International Journal of Performance Analysis in Sport","topic":"Sports Performance and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Sport Centre Pacific; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Bayesian probability; Computer science; Bayesian inference; Inference; Impulse response; Machine learning; Model parameter; Statistical inference; Artificial intelligence; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0108172,0.0007089468,0.001454731,0.001339742,0.0004050236,0.002297766,0.00217364,0.00178079,0.003708021],"category_scores_gemma":[0.0346673,0.000938703,0.00133195,0.001288579,0.001368743,0.001867625,0.001294112,0.002778564,0.0009599997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455107,"about_ca_system_score_gemma":0.001987681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01164759,"about_ca_topic_score_gemma":0.009648153,"domain_scores_codex":[0.9957997,0.002428951,0.0001248261,0.0007975423,0.0005586641,0.0002902368],"domain_scores_gemma":[0.9886422,0.009362006,0.0006497959,0.0006288282,0.0005539079,0.000163225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001318998,0.0001462118,0.01272745,0.0001279925,0.0002522878,0.00009695185,0.0003018942,0.79152,0.0009989411,0.1529112,0.002370697,0.03841434],"study_design_scores_gemma":[0.0000238013,0.0000448838,0.005203968,0.00004604263,0.00003233198,0.00005291479,0.00004720579,0.9039685,0.0002693321,0.08890726,0.001356793,0.00004694702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0392942,0.0001917776,0.9549398,0.0007118939,0.0000418358,0.0000548655,0.0003958817,0.0004132496,0.003956434],"genre_scores_gemma":[0.8382785,0.0004960221,0.1531962,0.0003718864,0.00009397355,0.0003091178,0.001125614,0.0001960951,0.005932575],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01164759,"threshold_uncertainty_score":0.05720752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03073714037226469,"score_gpt":0.3158014231540124,"score_spread":0.2850642827817477,"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."}}