{"id":"W4412947226","doi":"10.1007/s12283-025-00511-w","title":"Insightful skiing: developing explainable models of on-snow performance through physical attribute selection of alpine skis","year":2025,"lang":"en","type":"article","venue":"Sports Engineering","topic":"Winter Sports Injuries and Performance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Theratechnologies (Canada); Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Computer science; Snow; Selection (genetic algorithm); Rank (graph theory); Set (abstract data type); Feature selection; Machine learning; Resampling; Data mining; Artificial intelligence; Mathematics; Meteorology","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":[0.001463484,0.001187114,0.0004898924,0.001025266,0.0002940679,0.001025015,0.0008861991,0.0005813939,0.002628519],"category_scores_gemma":[0.003485848,0.0003167221,0.001143146,0.0005520099,0.000326858,0.0005252035,0.0006838203,0.0007826109,0.0005976899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005432713,"about_ca_system_score_gemma":0.0007550979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01414235,"about_ca_topic_score_gemma":0.01373768,"domain_scores_codex":[0.9997174,0.0001179314,0.00001369877,0.00008385186,0.00003097054,0.00003613023],"domain_scores_gemma":[0.9990185,0.0006275699,0.0001325556,0.00007299166,0.0001109757,0.00003734102],"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":[0.0001519085,0.0002321248,0.04442592,0.00008651696,0.0002396104,0.0001422268,0.0002436329,0.8820427,0.001576851,0.003124555,0.001975075,0.06575884],"study_design_scores_gemma":[0.000004417081,0.00003154315,0.00623312,0.00001561842,0.00001880406,0.000009850094,0.00002248883,0.9912579,0.0001407893,0.001941141,0.0003156869,0.00000875915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5164677,0.0003405694,0.4747459,0.0005479777,0.00007070081,0.0001836692,0.002304554,0.001301088,0.004037871],"genre_scores_gemma":[0.9684466,0.0001512786,0.02801452,0.00004445752,0.00003697727,0.0001512344,0.001612818,0.00005391486,0.001488219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01414235,"threshold_uncertainty_score":0.0281201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286699714473171,"score_gpt":0.2501830712993537,"score_spread":0.237316074154622,"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."}}