{"id":"W2020708932","doi":"10.1016/j.aap.2009.04.005","title":"Collision prediction models using multivariate Poisson-lognormal regression","year":2009,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":232,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Univariate; Multivariate statistics; Statistics; Goodness of fit; Poisson regression; Poisson distribution; Collision; Count data; Log-normal distribution; Mathematics; Econometrics; Regression analysis; Computer science; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007033079,0.0011144,0.001855533,0.001813302,0.0007099856,0.001526618,0.004758934,0.001604011,0.004527916],"category_scores_gemma":[0.01548256,0.001354161,0.001994469,0.002449225,0.000602568,0.002352558,0.001275913,0.00221018,0.001578506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317845,"about_ca_system_score_gemma":0.001510997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03449941,"about_ca_topic_score_gemma":0.02526789,"domain_scores_codex":[0.9974872,0.001184491,0.0001701835,0.0004831909,0.0003796351,0.0002952206],"domain_scores_gemma":[0.9863131,0.010135,0.0009205901,0.0006611327,0.001744166,0.0002259523],"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":[0.000264142,0.0001363908,0.008689398,0.00003935267,0.0001469034,0.00006383733,0.00006707356,0.9565425,0.0001415849,0.006837573,0.001262801,0.02580838],"study_design_scores_gemma":[0.000006976476,0.00001033992,0.0004553212,0.000002098223,0.00001019117,0.000006970523,0.000004941512,0.9980994,0.0000283569,0.001303799,0.00006513531,0.000006546914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1997894,0.0007294329,0.7940845,0.0005355532,0.0002156596,0.0001488714,0.001059656,0.001497073,0.001939781],"genre_scores_gemma":[0.9239134,0.0005241788,0.06480184,0.00009846699,0.0001379723,0.0002344492,0.001593523,0.0001730955,0.008523026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03449941,"threshold_uncertainty_score":0.0685972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249341216746714,"score_gpt":0.273504676697108,"score_spread":0.2510112645296408,"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."}}