{"id":"W3136409717","doi":"10.5383/jttm.03.01.002","title":"Predicting Severity of Accidents in Malaysia By Ordinal Logistic Regression Models","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Logistic regression; Ordered logit; Ordinal regression; Road accident; Accident (philosophy); Regression analysis; Transport engineering; Geography; Statistics; Environmental health; Medicine; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004844977,0.001042722,0.0005692467,0.002518512,0.0002730128,0.001588317,0.0009195755,0.0004607695,0.001543566],"category_scores_gemma":[0.01443387,0.0004687638,0.0009799644,0.001928987,0.0002705909,0.001276562,0.001140034,0.001448579,0.0005489277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005537041,"about_ca_system_score_gemma":0.0009592322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009417241,"about_ca_topic_score_gemma":0.006451393,"domain_scores_codex":[0.9971569,0.001735898,0.0002477268,0.0002884603,0.0002918267,0.0002792491],"domain_scores_gemma":[0.9904429,0.005951065,0.001994514,0.0003742795,0.0008519138,0.0003852803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005189492,0.0005802388,0.8926935,0.0001170502,0.0003773553,0.0002686123,0.0002126117,0.07188068,0.000393479,0.0008810541,0.001020693,0.03105577],"study_design_scores_gemma":[0.00002148849,0.0003722074,0.1363524,0.00006512467,0.000127304,0.0001702649,0.0004799522,0.8598508,0.0003774931,0.001470227,0.0006625551,0.00005020843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706937,0.0003600388,0.0264648,0.0003266099,0.00004460217,0.00007476327,0.001007593,0.0001753125,0.0008526],"genre_scores_gemma":[0.9927497,0.0001970227,0.005763504,0.00001234523,0.00001438011,0.00004458061,0.0006534824,0.00001102534,0.0005538741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009417241,"threshold_uncertainty_score":0.02562302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138206723839174,"score_gpt":0.248532548810862,"score_spread":0.2371504815724702,"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."}}