{"id":"W2061334190","doi":"10.1016/j.aap.2014.06.008","title":"Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario","year":2014,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Transports; Transport Canada","keywords":"Crash; Truck; Heteroscedasticity; Poison control; Injury prevention; Logistic regression; Occupational safety and health; Transport engineering; Engineering; Logit; Human factors and ergonomics; Environmental health; Automotive engineering; Econometrics; Computer science; Statistics; Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004765292,0.0003473514,0.0004292418,0.001708695,0.002501612,0.0008964681,0.001131458,0.0004321338,0.00193727],"category_scores_gemma":[0.002404035,0.0004834936,0.0008758695,0.002805774,0.0006825953,0.0004999764,0.001152556,0.0004301157,0.0002716312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01739926,"about_ca_system_score_gemma":0.01538304,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9832856,"about_ca_topic_score_gemma":0.9939142,"domain_scores_codex":[0.9989523,0.0001146152,0.0000988897,0.000124601,0.0003688712,0.0003407203],"domain_scores_gemma":[0.9974543,0.0001682123,0.0005830249,0.00007919642,0.001193202,0.0005220019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007627982,0.00002901142,0.9967778,0.00001725084,0.00003780272,0.00007812941,0.001080667,0.00008596022,0.0002311697,0.00002868497,0.0001895495,0.001367609],"study_design_scores_gemma":[0.000001752624,0.00001642941,0.998485,0.000004355616,0.000007856725,0.00001862915,0.001230855,0.00007607091,0.00002072142,0.000004766526,0.0001304145,0.000003004313],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982609,0.00006880453,0.00005246526,0.00004385167,0.000002419386,0.00002364901,0.0007122531,0.000003236305,0.0008324649],"genre_scores_gemma":[0.997987,0.0001065038,0.00007382093,0.00001541246,0.000001985008,0.00001417074,0.0007210413,0.000002389044,0.001077678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01739926,"threshold_uncertainty_score":0.126241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009517761399103864,"score_gpt":0.2194321818831339,"score_spread":0.20991442048403,"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."}}