{"id":"W2150480266","doi":"10.1016/j.aap.2015.07.009","title":"Developing crash modification functions for pedestrian signal improvement","year":2015,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Crash; Context (archaeology); Bayes' theorem; Poison control; Observational study; Variable (mathematics); Computer science; Transport engineering; Engineering; Bayesian probability; Statistics; Medicine; Artificial intelligence; Mathematics; Geography","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":[0.0009450192,0.001781295,0.0006864363,0.00128084,0.0005069504,0.0008184056,0.001458065,0.0007206124,0.006631274],"category_scores_gemma":[0.003667125,0.0004999497,0.0007449591,0.0004846342,0.0002761674,0.001347204,0.0008112129,0.0006837298,0.002313523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000596052,"about_ca_system_score_gemma":0.0009901626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005582275,"about_ca_topic_score_gemma":0.003849424,"domain_scores_codex":[0.9994485,0.0001200084,0.00003788676,0.0001339989,0.0001591238,0.0001003963],"domain_scores_gemma":[0.9979234,0.0005039476,0.0001823209,0.0003082086,0.0009904514,0.00009154855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001201738,0.00105956,0.01745947,0.0003398002,0.0001812312,0.000289542,0.000450883,0.09214099,0.1016027,0.003598512,0.006554222,0.7751213],"study_design_scores_gemma":[0.00008775908,0.000800019,0.007741397,0.0000384323,0.0002148186,0.0002273563,0.0002273594,0.8151405,0.1655659,0.001590823,0.00830771,0.00005792925],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07303894,0.0001240507,0.8987547,0.0001058943,0.00003521853,0.0003737698,0.0001900647,0.02388904,0.003488344],"genre_scores_gemma":[0.5639679,0.0001288453,0.4302851,0.00008171555,0.00002673853,0.000209537,0.00044351,0.0006629729,0.004193659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006631274,"threshold_uncertainty_score":0.02218384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05722710102476587,"score_gpt":0.2978234333153822,"score_spread":0.2405963322906164,"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."}}