{"id":"W4399624640","doi":"10.3390/app14125137","title":"AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Sports Performance and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Accelerometer; Artificial neural network; Computer science; Isometric exercise; Torque; Artificial intelligence; Machine learning; Medicine; Physical therapy; Physics","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":[0.0009825133,0.0007423759,0.0004270186,0.0006184315,0.0001179495,0.0005784582,0.0003206145,0.0003767794,0.001175165],"category_scores_gemma":[0.004733248,0.0001917318,0.0002852067,0.0004680605,0.00009726387,0.0003587079,0.0002590162,0.0004216267,0.0006076999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152259,"about_ca_system_score_gemma":0.0002642895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006120448,"about_ca_topic_score_gemma":0.005699437,"domain_scores_codex":[0.9996934,0.0000927513,0.00002637024,0.00009287318,0.00006233159,0.00003234454],"domain_scores_gemma":[0.998481,0.0009824877,0.0001473645,0.00007209869,0.0002777322,0.00003935591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001428191,0.0006265862,0.1552888,0.0003914707,0.000291657,0.0002382998,0.0002300435,0.3820488,0.03261814,0.0005280797,0.00139768,0.4249122],"study_design_scores_gemma":[0.000009964029,0.0001821925,0.04640537,0.00002756922,0.00002970514,0.00006900253,0.00002614878,0.9501025,0.00256338,0.0001779253,0.0003875813,0.0000186267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7731491,0.001222609,0.2181126,0.0002008963,0.0001600484,0.00008220816,0.0009548521,0.001171081,0.004946558],"genre_scores_gemma":[0.9844602,0.0001751916,0.013429,0.00002753147,0.00002533989,0.00003066677,0.0004916262,0.00001906695,0.001341418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006120448,"threshold_uncertainty_score":0.01216966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04488044652436429,"score_gpt":0.2954684262338211,"score_spread":0.2505879797094568,"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."}}