{"id":"W4390176075","doi":"10.3390/bioengineering11010015","title":"Validation of Automatically Quantified Swim Stroke Mechanics Using an Inertial Measurement Unit in Paralympic Athletes","year":2023,"lang":"en","type":"article","venue":"Bioengineering","topic":"Sports Performance and Training","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"Mitacs","keywords":"Intraclass correlation; Inertial measurement unit; Stroke (engine); Elite athletes; Limits of agreement; Accelerometer; Physical medicine and rehabilitation; Bland–Altman plot; Population; Mathematics; Athletes; Medicine; Statistics; Physical therapy; Computer science; Physics; Artificial intelligence; Nuclear medicine; Reproducibility","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004691328,0.0001022982,0.0002088441,0.000323226,0.00001970925,0.000008953599,0.00004895513,0.00005846745,0.00001181692],"category_scores_gemma":[0.00004888899,0.00009935359,0.00003788821,0.0004051736,0.000007863682,0.0001127219,0.00001717537,0.00008054657,0.000005166882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004167375,"about_ca_system_score_gemma":0.00006256338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003345901,"about_ca_topic_score_gemma":0.000006725476,"domain_scores_codex":[0.9990252,0.000005232169,0.0003148434,0.0001307128,0.0003145254,0.0002094936],"domain_scores_gemma":[0.9996574,0.000008818662,0.00004828933,0.000162828,0.00006462864,0.00005806733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000367734,0.00007275701,0.04174583,0.0002719329,0.00004616889,0.00002313624,0.0005431066,0.06989652,0.8854209,0.0003744098,0.000001411856,0.001567049],"study_design_scores_gemma":[0.0007641569,0.0001209609,0.08960342,0.0004086022,0.00004661479,0.000006519728,0.0002603372,0.7921324,0.1165054,0.00001214167,0.00002209129,0.0001173496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996049,0.00003366618,0.00340938,0.00002422998,0.0001487884,0.000151207,0.000001813637,0.0001597656,0.00002211402],"genre_scores_gemma":[0.9976501,0.00001163068,0.002207276,0.000009395041,0.00006316076,0.000006764373,0.00002488698,0.00002094472,0.000005856564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7689155,"threshold_uncertainty_score":0.4051522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1284607531831046,"score_gpt":0.3122320468552595,"score_spread":0.1837712936721549,"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."}}