{"id":"W2118070810","doi":"10.1007/11493648_2","title":"Biometric Gait Recognition","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":142,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Calgary","funders":"","keywords":"Biometrics; Gait; Computer science; Identification (biology); Gait analysis; Artificial intelligence; Computer vision; Pattern recognition (psychology); Physical medicine and rehabilitation; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000213874,0.0002870496,0.000290323,0.002223003,0.00006665532,0.0001451275,0.0003914075,0.0002216035,0.0005175068],"category_scores_gemma":[0.00003185052,0.0002828403,0.000119213,0.001082388,0.0001608918,0.00016298,0.00007323965,0.0004264185,0.0005150081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001957175,"about_ca_system_score_gemma":0.00004139992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003788001,"about_ca_topic_score_gemma":0.00003469646,"domain_scores_codex":[0.9985371,0.000005915489,0.0002876141,0.0004600635,0.0003943238,0.0003149775],"domain_scores_gemma":[0.9993474,0.0001087866,0.00005667713,0.0002803214,0.0001051279,0.0001016699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[5.163172e-7,0.000005586248,0.000005725681,0.00002135316,0.00001162761,0.000009818157,0.00003313942,0.02142751,0.0001129057,0.00002029259,0.00003578212,0.9783158],"study_design_scores_gemma":[0.0004374322,0.0000850443,0.0001923761,0.0006362149,0.00007889349,0.00007298401,2.292779e-7,0.9239954,0.005180042,0.04718658,0.02057352,0.001561221],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003331882,0.0006401751,0.9721748,0.00008795084,0.0007627154,0.000117305,0.00001766319,0.0002556936,0.02561047],"genre_scores_gemma":[0.7606159,0.001460489,0.2302189,0.001989702,0.003269415,0.00002033092,0.0002035599,0.0001946243,0.002027072],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9767545,"threshold_uncertainty_score":0.9999624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0194512992723186,"score_gpt":0.2209886885321086,"score_spread":0.20153738925979,"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."}}