{"id":"W2513806877","doi":"10.1016/j.aap.2016.07.033","title":"Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology","year":2016,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Mixed logit; Transport engineering; Poison control; Crash; Logit; Logistic regression; Human factors and ergonomics; Variable (mathematics); Segmentation; Population; Computer science; Injury prevention; Engineering; Computer security; Machine learning; Artificial intelligence; Environmental health; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004826497,0.0001716273,0.0002567497,0.0002358306,0.0002708436,0.00004149505,0.0002131474,0.0001035783,0.0003453125],"category_scores_gemma":[0.00007463708,0.0001034792,0.0002352699,0.0005303058,0.00006922881,0.0002262657,0.00006249,0.000118288,0.00003077447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358201,"about_ca_system_score_gemma":0.000009762832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001208844,"about_ca_topic_score_gemma":0.003402242,"domain_scores_codex":[0.9985306,0.0004134719,0.0003521387,0.0002799018,0.0001810983,0.0002428103],"domain_scores_gemma":[0.9991788,0.0002385343,0.0001208078,0.0003400996,0.00004259777,0.00007911745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001655909,0.00007291826,0.8846195,0.000004406443,0.002099478,0.00000172202,0.00240401,0.06268102,0.03268568,0.0005474644,0.0001550817,0.01471219],"study_design_scores_gemma":[0.0003393989,0.00005496225,0.9240077,0.00003630911,0.001139363,0.000003942617,0.0005281715,0.06977659,0.002731101,0.001046018,0.00009425249,0.0002422398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.874719,0.00008231094,0.1244238,0.0001056621,0.00017934,0.0001151468,6.236407e-7,0.0001744863,0.0001996074],"genre_scores_gemma":[0.9984663,0.00003478618,0.0009887916,0.00004353176,0.0001071499,0.00001551284,0.00001538511,0.0000189412,0.000309678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1237472,"threshold_uncertainty_score":0.4219759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459919575536833,"score_gpt":0.3249805833671847,"score_spread":0.1789886258135014,"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."}}