{"id":"W2104128206","doi":"10.1007/s12544-012-0072-y","title":"Sensitivity of a real-time freeway crash prediction model to calibration optimality","year":2012,"lang":"en","type":"article","venue":"European Transport Research Review","topic":"Traffic and Road Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crash; Calibration; Categorical variable; Sensitivity (control systems); Speed limit; Heuristic; Statistics; Computer science; Variable (mathematics); Set (abstract data type); Poison control; Engineering; Mathematics; Transport engineering; Artificial intelligence","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":[0.005978059,0.0001480447,0.0003136084,0.00008281478,0.00006871661,0.000006019039,0.0001373511,0.00003861168,0.0001166783],"category_scores_gemma":[0.00004909595,0.000131549,0.0001133786,0.0004100604,0.00005695661,0.0002615279,0.00002246667,0.0002895171,0.0002078378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005374327,"about_ca_system_score_gemma":0.00003312757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002532238,"about_ca_topic_score_gemma":0.000008572934,"domain_scores_codex":[0.9977542,0.0006378477,0.0004580235,0.000195204,0.0005058022,0.0004489369],"domain_scores_gemma":[0.9990901,0.00006038041,0.00002720702,0.00040618,0.0001179033,0.0002982753],"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":[0.0001858074,0.001226035,0.02959144,0.04040389,0.0004357356,0.0001870159,0.004914728,0.5524767,0.1321469,0.002714602,0.1113374,0.1243798],"study_design_scores_gemma":[0.0006940585,0.0002386961,0.6580361,0.00843892,0.0002280394,0.00002750192,0.00003599847,0.2674322,0.003248008,0.00001855216,0.06062054,0.0009813932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.434783,0.02889457,0.2817617,0.0009961948,0.0005142404,0.005021014,0.0009989964,0.002225651,0.2448047],"genre_scores_gemma":[0.9705585,0.02644327,0.002304076,0.00003170844,0.0001481251,0.00001389678,0.0001031944,0.00005771968,0.0003394739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6284447,"threshold_uncertainty_score":0.5364411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07306823615394333,"score_gpt":0.3098737344685442,"score_spread":0.2368054983146008,"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."}}