{"id":"W1977025899","doi":"10.3141/1840-08","title":"Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":306,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crash; Traffic flow (computer networking); Statistical model; Probabilistic logic; Poison control; Engineering; Computer science; Transport engineering; Computer security; Machine learning; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004364498,0.0002569777,0.0004207547,0.001128812,0.000339494,0.00006849043,0.0006247661,0.000260487,0.00007876661],"category_scores_gemma":[0.0001174735,0.0002226545,0.0003284576,0.00191923,0.0001514401,0.0005217589,0.000002570766,0.001215514,0.00002576569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004495441,"about_ca_system_score_gemma":0.0004384473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003191684,"about_ca_topic_score_gemma":0.01777412,"domain_scores_codex":[0.9948591,0.0005107682,0.001459209,0.0003991821,0.001919407,0.000852306],"domain_scores_gemma":[0.9971353,0.0004803852,0.0001753662,0.0004138676,0.001433979,0.0003610714],"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":[0.0005161655,0.0002659277,0.01685924,0.0002816786,0.00008205909,0.000007273406,0.00255717,0.9488176,0.01280828,0.001631492,0.005902741,0.01027036],"study_design_scores_gemma":[0.003685674,0.0007718775,0.710332,0.0004913844,0.0000768366,7.934142e-7,0.001037983,0.2689509,0.002138151,0.004736528,0.007355523,0.0004223517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9085227,0.00005592747,0.08776681,0.000528964,0.0003102707,0.002331962,0.0001224263,0.00009625919,0.0002646462],"genre_scores_gemma":[0.9893381,0.0007259822,0.008415114,0.00001036521,0.0001136327,0.0004256505,0.00005023651,0.00008914553,0.0008317507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6934727,"threshold_uncertainty_score":0.9918377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04360518644812779,"score_gpt":0.338428962292591,"score_spread":0.2948237758444632,"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."}}