{"id":"W1990102774","doi":"10.1061/jhtrcq.0000232","title":"Run-off-road Accident Prediction Model for Two-lane Highway","year":2008,"lang":"en","type":"article","venue":"Journal of Highway and Transportation Research and Development (English Edition)","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Negative binomial distribution; Poisson distribution; Traffic volume; Truck; Transport engineering; Poisson regression; Zero-inflated model; Traffic accident; Accident (philosophy); Statistics; Overdispersion; Engineering; Geography; Mathematics; Automotive engineering","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":[0.001525287,0.0008741023,0.001327957,0.001086682,0.0005837996,0.001218673,0.0022119,0.00113225,0.005107362],"category_scores_gemma":[0.002205376,0.0005415112,0.001348154,0.0008445245,0.0002551993,0.0009690787,0.0006962718,0.001182215,0.001446936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134872,"about_ca_system_score_gemma":0.001682748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04247161,"about_ca_topic_score_gemma":0.02388516,"domain_scores_codex":[0.999238,0.0002064628,0.00005358429,0.0002458885,0.0001263678,0.0001296095],"domain_scores_gemma":[0.998867,0.0004371369,0.0001262327,0.00003047598,0.0004902459,0.00004888922],"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":[0.0001383637,0.0001127411,0.01593496,0.0001434794,0.0001241331,0.0003783618,0.0001716334,0.9522151,0.0005275802,0.003869669,0.00255927,0.02382472],"study_design_scores_gemma":[0.000009326861,0.00004338086,0.002402643,0.00001061643,0.00003811216,0.00004472737,0.00002599772,0.9958412,0.00007995506,0.001069061,0.000421578,0.00001335811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4362581,0.002244305,0.5407206,0.001151912,0.0003023877,0.0003845541,0.0047867,0.002346169,0.0118053],"genre_scores_gemma":[0.9625265,0.001092562,0.01860744,0.00008234589,0.0001241054,0.0006214727,0.003697857,0.00008644452,0.01316128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04247161,"threshold_uncertainty_score":0.08444881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03007117624278696,"score_gpt":0.2635039233991544,"score_spread":0.2334327471563674,"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."}}