{"id":"W3033475622","doi":"10.70252/xygt9428","title":"Momentum During a Running Competition: A Sequential Explanatory Mixed-Methods Study","year":2020,"lang":"en","type":"article","venue":"International journal of exercise science","topic":"Sports Performance and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; Université du Québec à Rimouski; McGill University; Université de Montréal; Cégep de Rimouski; Université du Québec à Montréal","funders":"","keywords":"Momentum (technical analysis); Competition (biology); Distance running; Time trial; Selection (genetic algorithm); Psychology; Sample (material); Explanatory model; Sample size determination; Physical therapy; Econometrics; Demography; Medicine; Statistics; Computer science; Mathematics; Biology; Internal medicine; Economics; Machine learning; Ecology; Chemistry; Blood pressure; Sociology","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":[0.0008663901,0.00009139541,0.0002199274,0.0003265132,0.0001096578,0.0000785864,0.0004005806,0.00001824638,0.0001896021],"category_scores_gemma":[0.00006080713,0.00007737823,0.00008744873,0.000324854,0.0001498685,0.0006145808,0.0001070032,0.0002389247,0.00001234427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093307,"about_ca_system_score_gemma":0.0002912011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003566639,"about_ca_topic_score_gemma":2.352071e-7,"domain_scores_codex":[0.9981219,0.000007653821,0.0004282007,0.0001742739,0.001103061,0.0001649603],"domain_scores_gemma":[0.9988704,0.0000102028,0.0002602768,0.0000843419,0.0005400703,0.0002347538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003339298,0.001339805,0.7266925,0.0001206004,0.0004295452,0.008643133,0.03763457,0.001211007,0.1810682,0.0005615894,0.0004115084,0.03854825],"study_design_scores_gemma":[0.007508577,0.001294593,0.8879529,0.001337863,0.0002044424,0.002597785,0.02581464,0.003175389,0.06851909,0.0001075791,0.00115698,0.0003301381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964736,0.00009738204,0.0005234872,0.0008969448,0.001395921,0.000105859,8.868706e-7,0.00001706868,0.000488927],"genre_scores_gemma":[0.9945803,0.00003919379,0.004605582,0.000209205,0.0005251378,0.000002760082,9.582341e-7,0.00000733249,0.00002951921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1612604,"threshold_uncertainty_score":0.3155393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03166269748285323,"score_gpt":0.3784140963512487,"score_spread":0.3467513988683955,"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."}}