{"id":"W2278745275","doi":"10.1016/j.aap.2016.12.019","title":"Using micro-simulation to investigate the safety impacts of transit design alternatives at signalized intersections","year":2017,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crash; Intersection (aeronautics); SAFER; Negative binomial distribution; Statistical model; Transit (satellite); Transport engineering; Poison control; Computer science; Traffic simulation; Set (abstract data type); Microsimulation; Engineering; Statistics; Public transport; Computer security; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004362848,0.0001407537,0.0002529508,0.000221794,0.0003941816,0.00006612023,0.0002659557,0.00005374874,0.0001269622],"category_scores_gemma":[0.0000474501,0.0001130511,0.0003286497,0.0002898986,0.00004339376,0.0003250589,0.00005137757,0.00007854828,0.00001051657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001368636,"about_ca_system_score_gemma":0.00001319077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003257976,"about_ca_topic_score_gemma":0.004555526,"domain_scores_codex":[0.9989774,0.0001281967,0.0003943205,0.0001681803,0.0001747275,0.0001572232],"domain_scores_gemma":[0.9992011,0.0000682987,0.0001975952,0.0003976698,0.00006632193,0.00006905209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006323703,0.00001336331,0.03382352,0.000003165903,0.00101556,5.665918e-7,0.0009194349,0.9261078,0.03656613,0.000008775271,0.00001925033,0.001459214],"study_design_scores_gemma":[0.0003902887,0.00002601701,0.4883493,0.00006779917,0.001557974,0.000001150688,0.00009374581,0.483333,0.02580018,0.000178602,0.00004609856,0.0001558277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.541169,0.00006681304,0.458388,0.00004621119,0.00007175745,0.0001682364,6.742013e-7,0.00003355737,0.00005569455],"genre_scores_gemma":[0.9976877,0.00005667893,0.002039349,0.0000100018,0.00004215157,0.000006883366,0.00001589209,0.00001585987,0.0001254762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4565187,"threshold_uncertainty_score":0.4610089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05872025153370058,"score_gpt":0.330987944756472,"score_spread":0.2722676932227714,"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."}}