{"id":"W2179056674","doi":"10.3141/2432-17","title":"Can Microsimulation be used to Estimate Intersection Safety?","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"VisSim; Microsimulation; Intersection (aeronautics); Crash; Transport engineering; Bayes' theorem; Traffic conflict; Traffic simulation; Predictive modelling; Poison control; Computer science; Engineering; Econometrics; Statistics; Bayesian probability; Mathematics; Traffic congestion; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00359006,0.0002561313,0.0004067867,0.000999455,0.0005344584,0.0001150888,0.0007409081,0.0001925122,0.0001497697],"category_scores_gemma":[0.0001362901,0.0002047962,0.0002979613,0.001800295,0.0002348266,0.0004071224,0.000005462316,0.001716024,0.00002939505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004136644,"about_ca_system_score_gemma":0.0001876417,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020786,"about_ca_topic_score_gemma":0.07080171,"domain_scores_codex":[0.9946058,0.0006550457,0.001227151,0.0003149637,0.002383557,0.0008134285],"domain_scores_gemma":[0.9966403,0.0006905852,0.0001621449,0.0004162132,0.001625392,0.0004653406],"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":[0.002106896,0.0002231724,0.1416218,0.0005353029,0.0002959659,0.00004474424,0.01389764,0.759809,0.02835686,0.00534407,0.01293488,0.03482966],"study_design_scores_gemma":[0.001578016,0.0005325477,0.9439261,0.0003555303,0.00004463075,7.731584e-7,0.0008990481,0.007647929,0.003982706,0.00102204,0.03973082,0.0002798699],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9675435,0.00004074016,0.02538536,0.004764598,0.001019618,0.0007834465,0.00005652068,0.000111184,0.0002949864],"genre_scores_gemma":[0.997339,0.0001079389,0.001820328,0.00005019455,0.0002299491,0.00003450638,0.00003001071,0.00007917639,0.0003089494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8023043,"threshold_uncertainty_score":0.9461538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05567744213012274,"score_gpt":0.3641112242392241,"score_spread":0.3084337821091013,"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."}}