{"id":"W2110182076","doi":"10.3141/2387-02","title":"Computer Vision Techniques for the Automated Collection of Cyclist Data","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Data collection; Roundabout; Computer science; Transport engineering; Data set; Set (abstract data type); Simulation; Cycling; Statistics; Artificial intelligence; Engineering; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003302053,0.0001830217,0.0003423468,0.0005176684,0.0005171862,0.00009586937,0.001478869,0.0001682613,0.0001296515],"category_scores_gemma":[0.00006566616,0.0001125399,0.0002299994,0.001401998,0.0004495072,0.0006974683,0.00001069873,0.001136255,0.000008683725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190131,"about_ca_system_score_gemma":0.0002191976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00298493,"about_ca_topic_score_gemma":0.007368622,"domain_scores_codex":[0.9959009,0.0003839873,0.001164328,0.0002525653,0.001769899,0.0005283732],"domain_scores_gemma":[0.994984,0.001453855,0.0002320764,0.0006689327,0.002502397,0.0001587303],"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":[0.002403307,0.0007055726,0.05851349,0.002009733,0.001168058,0.00003011258,0.004690815,0.07466866,0.02137086,0.001928699,0.6189891,0.2135216],"study_design_scores_gemma":[0.0009212568,0.0005742135,0.8584197,0.0003021577,0.00005975314,7.462821e-7,0.0005780747,0.1144281,0.002148716,0.0004912528,0.02192323,0.0001528358],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9171427,0.0004610661,0.07313637,0.003819761,0.001129303,0.003587005,0.000261445,0.0003127847,0.0001495028],"genre_scores_gemma":[0.9882722,0.001335565,0.009758092,0.00001692335,0.0002159606,0.0001190036,0.00005747635,0.00005549732,0.00016924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7999062,"threshold_uncertainty_score":0.4936524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07124195240884161,"score_gpt":0.3772608425433464,"score_spread":0.3060188901345048,"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."}}