{"id":"W4320490804","doi":"10.7554/elife.81939","title":"Optimization of energy and time predicts dynamic speeds for human walking","year":2023,"lang":"en","type":"article","venue":"eLife","topic":"Sports Performance and Training","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Université Lille 1 - Sciences et Technologies","keywords":"Preferred walking speed; Work (physics); Trajectory; Energy (signal processing); Steady state (chemistry); Power walking; Simulation; Computer science; Energy expenditure; Transient (computer programming); Task (project management); Effect of gait parameters on energetic cost; Control theory (sociology); Gait; Physical medicine and rehabilitation; Mathematics; Physics; Statistics; Engineering; Artificial intelligence; Biology; Gait analysis","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":[0.0001176407,0.00004623424,0.0001138493,0.00009883692,0.00004107617,0.000003427512,0.00001773117,0.00003331725,0.00003623973],"category_scores_gemma":[0.00001116294,0.00004217113,0.00002168511,0.0001128183,0.00002060306,0.00004120046,0.00001208195,0.00002056264,0.000002427769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007692843,"about_ca_system_score_gemma":0.00001705518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004583709,"about_ca_topic_score_gemma":0.000001659747,"domain_scores_codex":[0.9995967,0.000001312676,0.0001208123,0.00008291109,0.00009960747,0.00009872135],"domain_scores_gemma":[0.9998051,0.00001183702,0.00003994938,0.00007157231,0.00003812896,0.00003343025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00109581,0.0004987065,0.6512829,0.002515464,0.001004012,0.0001674786,0.01204985,0.06047256,0.09763367,0.007424712,0.02072379,0.145131],"study_design_scores_gemma":[0.003326015,0.0006800276,0.2310702,0.0003951584,0.0001532926,0.00002490906,0.0002740186,0.7535116,0.006866811,0.0001327877,0.003384461,0.0001806699],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968759,0.00006350202,0.001038647,0.00009309989,0.00005331228,0.00009830442,0.000003883556,0.00008212886,0.001691171],"genre_scores_gemma":[0.9968452,0.00006448167,0.001032178,0.000102273,0.0000900805,0.000007640089,0.0002004018,0.00001354185,0.001644182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6930391,"threshold_uncertainty_score":0.1719689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609493171496625,"score_gpt":0.2822900202639111,"score_spread":0.2661950885489449,"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."}}