{"id":"W2277193729","doi":"10.1007/s40534-016-0096-4","title":"Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation","year":2016,"lang":"en","type":"article","venue":"Journal of Modern Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Waterloo","funders":"","keywords":"Multinomial logistic regression; Collision; Logistic regression; Mixed logit; Statistics; Econometrics; Multilevel model; Computer science; Logit; Multinomial distribution; Mathematics; Computer security","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":[0.0002703494,0.0001034872,0.0004001331,0.0001719592,0.00002178671,0.000003643416,0.0002105295,0.00009471818,0.000001738954],"category_scores_gemma":[0.00002981102,0.00006816615,0.00005715923,0.0001383308,0.00003903103,0.0005539925,0.000007816231,0.00009403958,1.083815e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002091651,"about_ca_system_score_gemma":0.00002691264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008956472,"about_ca_topic_score_gemma":0.00004580695,"domain_scores_codex":[0.9987881,0.00003486667,0.0006733164,0.0001265864,0.0002983391,0.00007879687],"domain_scores_gemma":[0.9989469,0.0001434439,0.0004278299,0.0002914456,0.0001392148,0.00005116145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004420593,0.0001893827,0.02232149,0.0007094169,0.0005957424,0.000006247131,0.002075538,0.7166991,0.02035446,0.00006899169,0.0001241127,0.2364135],"study_design_scores_gemma":[0.0007391234,0.00007369948,0.1749892,0.0005455391,0.0006797509,9.065952e-7,0.00002939935,0.8135635,0.008621396,0.0006717619,0.0000057287,0.00007992647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5220415,0.000767757,0.476936,0.00001097448,0.00006746156,0.00005255389,0.000112328,0.000008763764,0.000002697483],"genre_scores_gemma":[0.9947171,0.0007834246,0.004390477,0.000001408185,0.0000206553,4.619357e-7,0.00007249627,0.00001025555,0.000003672865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4726757,"threshold_uncertainty_score":0.2779735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837490287601782,"score_gpt":0.2986560194257104,"score_spread":0.2602811165496925,"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."}}