{"id":"W2786829978","doi":"10.1109/ssci.2017.8285250","title":"Utilizing gait traits to improve e-border watchlist performance","year":2017,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Patras; University of Calgary","keywords":"Gait; Biometrics; Metadata; Computer science; Identification (biology); Feature extraction; Gait analysis; Inference; Artificial intelligence; Feature (linguistics); Data mining; Physical medicine and rehabilitation; World Wide Web","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005402286,0.00009693253,0.0001105423,0.00004884636,0.0001734464,0.0001408457,0.0001745386,0.00003583397,0.001032202],"category_scores_gemma":[0.00001823786,0.00008594333,0.00005428748,0.00004464287,0.00001311703,0.000190106,0.00002813052,0.0000735208,0.0006343168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001520693,"about_ca_system_score_gemma":0.000004106156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002772961,"about_ca_topic_score_gemma":0.00007839035,"domain_scores_codex":[0.9994928,0.000002184877,0.0001106622,0.0001205656,0.00008284484,0.0001909757],"domain_scores_gemma":[0.9996352,0.000005376397,0.00001467464,0.0002124004,0.00003399296,0.00009840559],"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":[0.000007193415,0.00003442707,0.003277958,0.0001395323,0.0001299362,0.000005894829,0.0003949641,0.001759934,0.05168483,0.0003129717,0.007600694,0.9346517],"study_design_scores_gemma":[0.001578732,0.0001091075,0.2970435,0.0001516521,0.000140718,0.000007923225,0.0007340928,0.3203612,0.1614491,0.0001248363,0.2167153,0.001583888],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7262491,0.00001359995,0.002136403,0.0003074389,0.000180395,0.00006805771,0.000005311942,0.000232031,0.2708077],"genre_scores_gemma":[0.9921338,0.00002494658,0.001009812,0.000168997,0.00008208592,0.00001184531,0.000002705765,0.00001663855,0.006549172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9330678,"threshold_uncertainty_score":0.999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01481093023848107,"score_gpt":0.2564907508052133,"score_spread":0.2416798205667322,"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."}}