{"id":"W2027576351","doi":"10.3141/2393-04","title":"Use of Spatiotemporal Parameters of Gait for Automated Classification of Pedestrian Gender and Age","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; SNC-Lavalin (Canada)","funders":"","keywords":"Pedestrian; Statistic; Gait; Artificial intelligence; Cohen's kappa; Computer science; k-nearest neighbors algorithm; Statistical classification; Pattern recognition (psychology); Machine learning; Statistics; Engineering; Mathematics; Physical medicine and rehabilitation; Transport engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008662,0.0003555688,0.0003453079,0.002517369,0.0002015465,0.0005466023,0.0002259823,0.0002972284,0.0005455522],"category_scores_gemma":[0.003556654,0.0001633189,0.0002311176,0.0008983082,0.0001709544,0.0005666408,0.0002483376,0.0001682384,0.0003867058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001966739,"about_ca_system_score_gemma":0.0003767393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004560826,"about_ca_topic_score_gemma":0.010283,"domain_scores_codex":[0.9994023,0.0001983753,0.00005015226,0.0001092553,0.0002008432,0.00003907993],"domain_scores_gemma":[0.9980895,0.0005164435,0.0002942166,0.0001426238,0.0008836364,0.00007355898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006661243,0.0002098308,0.3566838,0.0002615968,0.0001334192,0.0002648786,0.000379529,0.006067127,0.0474175,0.0006778235,0.001669634,0.5855687],"study_design_scores_gemma":[0.00005112858,0.0008302891,0.7074416,0.0001812804,0.0002189747,0.002361986,0.0009785263,0.2467207,0.03459406,0.001227953,0.005264127,0.0001293433],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8732331,0.0008250708,0.1206398,0.0001186178,0.00007792439,0.0001054164,0.00108224,0.0005595226,0.003358324],"genre_scores_gemma":[0.9405474,0.000389542,0.0578361,0.0000226693,0.00002547063,0.00003052322,0.0005901158,0.00002030744,0.0005378397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004560826,"threshold_uncertainty_score":0.009068549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2191449884032817,"score_gpt":0.3710914674944848,"score_spread":0.1519464790912031,"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."}}