{"id":"W2070687669","doi":"10.1016/j.aap.2013.11.001","title":"Bayesian methodology to estimate and update safety performance functions under limited data conditions: A sensitivity analysis","year":2013,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Waterloo","funders":"","keywords":"Sensitivity (control systems); Bayesian probability; Prior probability; Data mining; Computer science; Calibration; Process (computing); Prior information; Machine learning; Engineering; Statistics; Artificial intelligence; Mathematics","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":[0.03240731,0.001733577,0.002613036,0.002605323,0.0007056574,0.002154046,0.002377299,0.002963236,0.002911179],"category_scores_gemma":[0.1079281,0.002372642,0.002962363,0.001425109,0.001868671,0.004769287,0.002463398,0.003176703,0.0002671125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002762676,"about_ca_system_score_gemma":0.00218369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0149763,"about_ca_topic_score_gemma":0.007749708,"domain_scores_codex":[0.9903492,0.006839785,0.0003862723,0.0008140413,0.001270461,0.0003402595],"domain_scores_gemma":[0.8619332,0.1286984,0.002597532,0.003130309,0.003304603,0.0003360239],"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":[0.0001560495,0.00008554252,0.001019934,0.00008143723,0.0002447678,0.00005176549,0.00004397871,0.9782522,0.0005525253,0.009598974,0.0002685593,0.009644335],"study_design_scores_gemma":[0.00004040855,0.00005856638,0.0005359398,0.00002171439,0.00005951993,0.00002582718,0.000009132902,0.9895619,0.0004968607,0.009011024,0.0001495591,0.00002960376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06428543,0.0008945259,0.9322393,0.0005196725,0.00004296714,0.0001649682,0.0002317516,0.0002070357,0.001414375],"genre_scores_gemma":[0.7936566,0.0009612742,0.2010703,0.0003212231,0.00008616473,0.0005135141,0.0006262084,0.000170921,0.002593704],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03240731,"threshold_uncertainty_score":0.1713883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03357965746913422,"score_gpt":0.3134108429157025,"score_spread":0.2798311854465683,"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."}}