{"id":"W4285799949","doi":"10.1101/2022.07.15.500158","title":"Optimization of energy and time determines dynamic speeds for human walking","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sports Performance and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Preferred walking speed; Trajectory; Work (physics); Energy (signal processing); Simulation; Energy expenditure; Computer science; Power walking; Transient (computer programming); Task (project management); Energy cost; Effect of gait parameters on energetic cost; Control theory (sociology); Mathematics; Gait; Physical medicine and rehabilitation; Statistics; Engineering; Physics; Artificial intelligence","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":[0.0002215566,0.0002233508,0.0002291582,0.0002813734,0.0001876723,0.0005432474,0.0001557835,0.0003216971,0.001232358],"category_scores_gemma":[0.001136074,0.0002379674,0.0002302458,0.0002168056,0.0003218953,0.0004200641,0.0002243954,0.0002010624,0.0002491498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003214515,"about_ca_system_score_gemma":0.0002618451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002936268,"about_ca_topic_score_gemma":0.002094126,"domain_scores_codex":[0.9999372,0.00001578156,0.000003202878,0.00002511494,0.000007253044,0.00001140061],"domain_scores_gemma":[0.9997968,0.00008951382,0.00004510052,0.00002125939,0.00002480743,0.00002259139],"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":[0.0004664504,0.0001322448,0.02799406,0.0001314885,0.00007519733,0.0001282286,0.0002185648,0.8754964,0.05254557,0.01421191,0.001012354,0.02758746],"study_design_scores_gemma":[0.00001375778,0.0001252183,0.01997484,0.00001362564,0.00001121987,0.00005660046,0.00005254548,0.9689051,0.003094503,0.00726914,0.0004613449,0.00002209078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9083248,0.0001775842,0.08831457,0.0001526104,0.00001091524,0.00002137896,0.0002226819,0.0000941052,0.002681318],"genre_scores_gemma":[0.9957958,0.00004684069,0.003580146,0.000008190788,0.000001851262,0.00001591264,0.00006161868,0.00001427878,0.0004753304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002936268,"threshold_uncertainty_score":0.005838394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298005615880483,"score_gpt":0.2448481899321008,"score_spread":0.231868133773296,"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."}}