{"id":"W4285030286","doi":"10.1038/s41597-022-01495-z","title":"The Toronto older adults gait archive: video and 3D inertial motion capture data of older adults’ walking","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"Government of Canada; CIHR Skin Research Training Centre; AGE-WELL","keywords":"Gait; Motion capture; Physical medicine and rehabilitation; Motion (physics); Computer science; Medicine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003712869,0.0001551611,0.000203166,0.00005569217,0.002625897,0.00007641789,0.002241531,0.00007485929,0.000306993],"category_scores_gemma":[0.0003190289,0.0001182577,0.00002915481,0.0002364253,0.0002352714,0.0008077252,0.005324808,0.0005013458,0.00002513769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007374726,"about_ca_system_score_gemma":0.0001769164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002691127,"about_ca_topic_score_gemma":0.01265232,"domain_scores_codex":[0.9965203,0.0007292909,0.000572004,0.001065324,0.0006289413,0.0004841322],"domain_scores_gemma":[0.99562,0.0002649293,0.0003750787,0.003525835,0.0001073179,0.0001068373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000373525,0.0006099931,0.004520962,0.0006869718,0.00006041491,0.000004888457,0.02068138,0.000001079102,0.002525083,0.0005365467,0.8020335,0.1679657],"study_design_scores_gemma":[0.00517275,0.00006310163,0.5312619,0.001392574,0.0001441084,0.00001128186,0.04425472,0.03817528,0.000006486524,0.000457837,0.3785366,0.0005233365],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8403586,0.01526612,0.004326355,0.003009864,0.03077385,0.00708544,0.09236861,0.0003523286,0.006458889],"genre_scores_gemma":[0.9579988,0.0001899199,0.0006963735,0.000204044,0.0003767431,0.00009899458,0.03606427,0.00003060427,0.004340272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5267409,"threshold_uncertainty_score":0.9986725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02903593195237551,"score_gpt":0.3376554024780065,"score_spread":0.308619470525631,"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."}}