{"id":"W2998046645","doi":"10.1016/j.aap.2019.105395","title":"Analyzing the ability of crash-prone highways to handle stochastically modelled driver demand for stopping sight distance","year":2019,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Sight; Crash; Computer science; Simulation; Monte Carlo method; Transport engineering; Driving simulator; Poison control; Engineering; Statistics; 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":[0.0005218719,0.0001539973,0.0003937247,0.0001567548,0.000097816,0.00003153158,0.0002259744,0.00006565538,0.0001003245],"category_scores_gemma":[0.00003295294,0.0001185525,0.0004172574,0.0006123419,0.00001999747,0.000169516,0.00004846961,0.00008758574,0.00001677327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008406807,"about_ca_system_score_gemma":0.00001313236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000011691,"about_ca_topic_score_gemma":0.0004447371,"domain_scores_codex":[0.9987698,0.00005047138,0.0004711003,0.0002821212,0.0002023321,0.0002242301],"domain_scores_gemma":[0.999175,0.0001415308,0.0001006619,0.0004155462,0.000107256,0.00006007023],"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":[0.00004259695,0.00004262463,0.04163024,0.00002245086,0.0008992952,1.260693e-7,0.0002931172,0.946015,0.008329858,0.0005136451,0.00007194167,0.002139116],"study_design_scores_gemma":[0.0004490928,0.00004591384,0.2484002,0.00005205558,0.001344861,1.308993e-7,0.00006375905,0.7478406,0.0009843602,0.0005468361,0.0000896272,0.0001826452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4662824,0.000139705,0.5329887,0.00004080642,0.00005823075,0.0003645425,0.000001044124,0.00005742746,0.00006720429],"genre_scores_gemma":[0.997535,0.00002601647,0.002111607,0.000005270723,0.00003868694,0.00005236448,0.00003490075,0.00001651781,0.000179615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5312527,"threshold_uncertainty_score":0.4834429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008245510028089329,"score_gpt":0.2354139958979292,"score_spread":0.2271684858698399,"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."}}