{"id":"W3165515738","doi":"10.3389/fphys.2021.669884","title":"Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data","year":2021,"lang":"en","type":"article","venue":"Frontiers in Physiology","topic":"Sports Performance and Training","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Adidas (Canada)","funders":"Deutsche Forschungsgemeinschaft; Verein Deutscher Ingenieure","keywords":"Training (meteorology); Percentile; Medicine; Endurance training; Physical therapy; Training set; Physical medicine and rehabilitation; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101986,0.0002686864,0.0002967007,0.001063814,0.0002479836,0.0002565956,0.0002406332,0.0002305887,0.00098235],"category_scores_gemma":[0.002252182,0.000141007,0.0002802671,0.0007891407,0.0001928359,0.0002064851,0.0003813266,0.0001642368,0.0003976242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001353317,"about_ca_system_score_gemma":0.0002396414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173421,"about_ca_topic_score_gemma":0.002114887,"domain_scores_codex":[0.9990335,0.0002434784,0.0001470952,0.0002876263,0.0001929181,0.00009546188],"domain_scores_gemma":[0.997482,0.0004731965,0.0009067964,0.0003635561,0.0005555342,0.0002189703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003751563,0.00005718402,0.9940702,0.0000290224,0.00004289123,0.0000921451,0.0002174962,0.00007041312,0.001427889,0.000007766846,0.00008553432,0.003524236],"study_design_scores_gemma":[0.000001987925,0.0003447437,0.9986905,0.000005139903,0.00002015236,0.0001427968,0.0001584328,0.00008713258,0.0003107325,0.000003618312,0.0002315424,0.000003188329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987885,0.00008047908,0.000359754,0.000003979038,0.000002917108,0.00002282767,0.0005589518,0.000007879699,0.0001746477],"genre_scores_gemma":[0.9974088,0.00006753936,0.0003128437,0.000006494859,0.000005891238,0.00004764471,0.001772877,0.000007089552,0.0003706742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00173421,"threshold_uncertainty_score":0.005393624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03548464830871886,"score_gpt":0.2998315927133725,"score_spread":0.2643469444046537,"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."}}