{"id":"W6888902841","doi":"10.25358/openscience-482","title":"Explaining the unique nature of individual gait patterns with deep learning","year":2019,"lang":"en","type":"article","venue":"Gutenberg Open Science","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Banting and Best Diabetes Centre, University of Toronto; Deutsche Forschungsgemeinschaft","keywords":"Gait; Black box; Relevance (law); Variable (mathematics); Biomechanics; Gait analysis; Artificial neural network; Deep learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007008243,0.00009303293,0.0001362803,0.0001033495,0.0001400747,0.0002436469,0.001302617,0.00004321897,0.0002753897],"category_scores_gemma":[0.0000353003,0.0000587035,0.00002820788,0.0007683896,0.0001003486,0.0004752207,0.0002512498,0.0003427165,0.00004181648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000176965,"about_ca_system_score_gemma":0.00003803166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003385849,"about_ca_topic_score_gemma":0.0000629143,"domain_scores_codex":[0.9990404,0.00002618673,0.0001296471,0.0001983453,0.0003889109,0.0002164977],"domain_scores_gemma":[0.9995081,0.00005617871,0.00005320437,0.0002377701,0.00008929782,0.00005543732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003919773,0.0001030651,0.4500908,0.0002103515,0.0004236466,0.00003674808,0.02304828,0.3200502,0.04344034,0.008172854,0.0005170163,0.1538675],"study_design_scores_gemma":[0.002179404,0.0004342087,0.6556395,0.0008594425,0.0001976255,0.0001215124,0.03786094,0.2239054,0.066023,0.0003789501,0.01083108,0.001568873],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977613,0.0000297848,0.001845707,0.00007973336,0.00008395002,0.0001523421,0.000003891957,0.00004249206,0.02014911],"genre_scores_gemma":[0.9985837,0.0000151326,0.000934225,0.0001413776,0.00001278901,0.0000100364,0.000006172238,0.00001027041,0.0002862888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2055487,"threshold_uncertainty_score":0.3015324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009684967416757477,"score_gpt":0.2409754527680147,"score_spread":0.2312904853512573,"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."}}