{"id":"W2028400898","doi":"10.1016/j.aap.2009.06.025","title":"Accident prediction models with random corridor parameters","year":2009,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariate; Random effects model; Markov chain Monte Carlo; Statistics; Context (archaeology); Goodness of fit; Poisson distribution; Econometrics; Bayes' theorem; Bayesian inference; Poison control; Inference; Bayesian probability; Computer science; Mathematics; Geography; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000262248,0.0002226865,0.0003688417,0.0003538859,0.00009941602,0.00007627357,0.0001659471,0.00009904795,0.0001097553],"category_scores_gemma":[0.00001027243,0.0001896025,0.0003805721,0.0008401613,0.00001387103,0.0006173514,0.0000126392,0.0001506342,0.0000292445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001138548,"about_ca_system_score_gemma":0.00001307752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002381647,"about_ca_topic_score_gemma":0.0005944381,"domain_scores_codex":[0.9985788,0.00005745106,0.000434094,0.0002953901,0.0003637621,0.0002704653],"domain_scores_gemma":[0.9993477,0.00002307785,0.00009806448,0.0003433218,0.00008839652,0.00009946089],"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":[0.0001037207,0.00005893865,0.04152442,0.000001430906,0.001093096,0.000003815667,0.0001265734,0.9398896,0.00004176065,0.00008695244,0.0009377531,0.01613197],"study_design_scores_gemma":[0.001974561,0.0001596342,0.4274195,0.00005164125,0.003706313,0.000004967494,0.0001149014,0.5643546,0.0004188353,0.001318835,0.0001363736,0.0003398435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5488658,0.0003007481,0.4490063,0.00004296421,0.000195805,0.0002755607,4.763293e-7,0.0004371881,0.0008751999],"genre_scores_gemma":[0.9971352,0.0003067386,0.001771968,0.00002935769,0.0000840838,0.00003001978,0.000167954,0.00001928055,0.0004553906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4482694,"threshold_uncertainty_score":0.7731766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009237510217089394,"score_gpt":0.2077999432746695,"score_spread":0.1985624330575801,"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."}}