{"id":"W4403128721","doi":"10.3390/s24196440","title":"A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Cadence; Ground reaction force; Biomechanics; Kinematics; Crank; Simulation; Gait; Cycling; Force platform; Gait analysis; Computer science; Physical medicine and rehabilitation; Artificial intelligence; Physics; Medicine","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":[0.0005676321,0.0007380202,0.0005680026,0.0008626024,0.0003206338,0.0004949211,0.0005191739,0.0008171257,0.0007119946],"category_scores_gemma":[0.002130312,0.0003431657,0.0004892512,0.0005790402,0.0001851948,0.0003451529,0.0003229998,0.0006450711,0.0002588627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003648041,"about_ca_system_score_gemma":0.0005830259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01092551,"about_ca_topic_score_gemma":0.007676259,"domain_scores_codex":[0.9997965,0.00003578774,0.00002441494,0.00007937662,0.00004178835,0.00002209875],"domain_scores_gemma":[0.9995505,0.0002529319,0.00004473489,0.00002414183,0.0001111671,0.00001651163],"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":[0.00009333916,0.0001340469,0.005962724,0.00006273174,0.0000713971,0.00008311278,0.00004603562,0.7917182,0.003341267,0.0004264863,0.0005682182,0.1974923],"study_design_scores_gemma":[0.000001396361,0.00001801257,0.0007441218,0.000004400757,0.00000382595,0.000007251968,0.000003707965,0.9987673,0.0001836364,0.0001902877,0.00007315231,0.000002970831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1656648,0.001334698,0.8294455,0.000245165,0.00008110755,0.0001196615,0.0002704377,0.0009398431,0.0018989],"genre_scores_gemma":[0.923066,0.0004169185,0.07405978,0.00009365832,0.00004641856,0.0001784711,0.000335668,0.00002781465,0.00177521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01092551,"threshold_uncertainty_score":0.02172381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756929407662552,"score_gpt":0.2988246419331102,"score_spread":0.2712553478564847,"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."}}