{"id":"W578162817","doi":"","title":"Analysis of Injury Severity of Drivers Involved in Single-Vehicle and Two-Vehicle Crashes on Ontario Highways Using Heteroscedastic Ordered Logit Models","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heteroscedasticity; Crash; Truck; Logistic regression; Logit; Econometrics; Mixed logit; Poison control; Motor vehicle crash; Statistics; Injury prevention; Transport engineering; Computer science; Engineering; Mathematics; Environmental health; Medicine; Automotive engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002961239,0.0003782103,0.0009155649,0.002369545,0.0002503932,0.00005259626,0.0004231031,0.0003008456,0.00006571755],"category_scores_gemma":[0.0001299032,0.0004014316,0.0002374536,0.003215511,0.0007682148,0.0006035414,0.00001575375,0.001217698,0.00000354605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003524774,"about_ca_system_score_gemma":0.0002270643,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05880154,"about_ca_topic_score_gemma":0.3620214,"domain_scores_codex":[0.9939251,0.0006554293,0.001352525,0.0007672557,0.002259843,0.001039839],"domain_scores_gemma":[0.9966418,0.001017763,0.0001736181,0.0004884851,0.001319367,0.0003589731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007699674,0.0003189256,0.3675466,0.0004426535,0.0003188638,0.00001508893,0.009818405,0.5967528,0.02167194,0.001009598,0.00004381206,0.001291387],"study_design_scores_gemma":[0.001576082,0.000572801,0.7827348,0.0003001819,0.0001448572,5.280033e-8,0.002274135,0.2046413,0.006883478,0.0004431063,0.00007202454,0.0003572508],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952779,0.00004813508,0.002912961,0.00007620644,0.00007302273,0.0007266183,0.0003412259,0.0001783324,0.0003655761],"genre_scores_gemma":[0.998181,0.0001019682,0.001367991,0.00001285242,0.00002751204,0.00004889215,0.0001578286,0.00006839075,0.00003357066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4151882,"threshold_uncertainty_score":0.9998438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05963188384815138,"score_gpt":0.3181701424778959,"score_spread":0.2585382586297446,"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."}}