{"id":"W2157272662","doi":"10.1115/1.4028410","title":"Using Entropy Measures to Characterize Human Locomotion","year":2014,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mashhad University of Medical Sciences","keywords":"Entropy (arrow of time); Sample entropy; Mathematics; Entropy rate; Computer science; Statistical physics; Artificial intelligence; Principle of maximum entropy; Statistics; Joint quantum entropy; Pattern recognition (psychology); Physics","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":[0.0004297784,0.0001619727,0.0003179498,0.000294567,0.00003416338,0.00005261514,0.0001933747,0.00008562877,0.0000386637],"category_scores_gemma":[0.00008365517,0.0001505304,0.0001430653,0.0002191847,0.000005080753,0.0001463076,0.00002173213,0.0002251887,0.000015656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239266,"about_ca_system_score_gemma":0.000007472066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002057781,"about_ca_topic_score_gemma":4.029344e-7,"domain_scores_codex":[0.9988523,0.00002471417,0.0005020378,0.00009039396,0.0002780875,0.0002524711],"domain_scores_gemma":[0.9994143,0.00002875457,0.00007588428,0.0001385942,0.0000855583,0.0002568877],"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":[0.000005017799,0.0000180038,0.000003406093,0.0000224768,0.00004175236,0.000004753756,0.00002440538,0.1667073,0.8255814,0.001164275,0.00004048152,0.006386798],"study_design_scores_gemma":[0.001095288,0.0002737285,0.0004944497,0.0002130348,0.00005709458,0.0001475938,0.00001114952,0.9035316,0.08532239,0.0002178191,0.008285103,0.0003507641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1563164,0.00003063614,0.8425078,0.0001206851,0.000821461,0.00007302745,9.404126e-7,0.00009644793,0.00003251039],"genre_scores_gemma":[0.9879693,0.000006892508,0.01132018,0.00005865832,0.0005970056,0.000001797096,7.909576e-7,0.00003837928,0.000006950318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8316529,"threshold_uncertainty_score":0.6138453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555293774258677,"score_gpt":0.2341413806279486,"score_spread":0.2085884428853619,"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."}}