{"id":"W2166247254","doi":"10.3141/2280-18","title":"Improving Transferability of Safety Performance Functions by Bayesian Model Averaging","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bayesian probability; Goodness of fit; Calibration; Model selection; Markov chain Monte Carlo; Computer science; Bayesian inference; Frequentist inference; Sample (material); Monte Carlo method; Covariate; Econometrics; Sampling (signal processing); Statistics; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03224849,0.001572719,0.001953816,0.002351836,0.0008279418,0.001958377,0.002600763,0.001182158,0.002052747],"category_scores_gemma":[0.08043196,0.0009572989,0.002702903,0.001841455,0.001214054,0.004449353,0.004175694,0.002980661,0.0004471543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00195766,"about_ca_system_score_gemma":0.002922113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01330322,"about_ca_topic_score_gemma":0.009323061,"domain_scores_codex":[0.9831639,0.01158339,0.0007140778,0.001879921,0.002201105,0.0004575833],"domain_scores_gemma":[0.9654198,0.02474284,0.002261891,0.004912559,0.002415224,0.0002477104],"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":[0.00009539891,0.0001481262,0.004968099,0.00008759783,0.0002711677,0.00007809977,0.0004456865,0.8500418,0.00111447,0.0381824,0.0005992592,0.1039679],"study_design_scores_gemma":[0.00001345539,0.00007601529,0.0008355258,0.00001797767,0.00003969101,0.00001743594,0.00003951328,0.9767943,0.0004989057,0.02112644,0.0005209452,0.00001972536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01732737,0.00006407476,0.98093,0.00009106217,0.000007297507,0.00007342041,0.00003505173,0.0003617996,0.001109914],"genre_scores_gemma":[0.5953202,0.0002252317,0.4019956,0.0001283962,0.00003846125,0.0004131129,0.0004264062,0.0003394062,0.001113255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03224849,"threshold_uncertainty_score":0.1705484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03835713747470788,"score_gpt":0.3003217661346378,"score_spread":0.26196462865993,"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."}}