{"id":"W4321502816","doi":"10.1016/j.ijtst.2023.02.005","title":"Application of Conditional Deep Generative Networks (CGAN) in empirical bayes estimation of road crash risk and identifying crash hotspots","year":2023,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Transport Canada","keywords":"Crash; Transferability; Bayes' theorem; Computer science; Range (aeronautics); Statistics; Artificial neural network; Machine learning; Algorithm; Econometrics; Artificial intelligence; Mathematics; Engineering; Bayesian probability","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002943024,0.0009010423,0.0008328005,0.001019283,0.0003312275,0.0005950828,0.001438885,0.001009495,0.00122615],"category_scores_gemma":[0.007748597,0.0005752073,0.0006799761,0.000516853,0.001019547,0.0009224069,0.001474069,0.001779774,0.0001795126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067863,"about_ca_system_score_gemma":0.0009917491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01385978,"about_ca_topic_score_gemma":0.01184241,"domain_scores_codex":[0.9991763,0.0004195331,0.0000338491,0.0001759125,0.0001232156,0.00007124947],"domain_scores_gemma":[0.9957488,0.00338318,0.000256608,0.0001896376,0.0003199718,0.000101768],"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":[0.00005528135,0.00002212795,0.002062265,0.00002396845,0.00004593671,0.00004800276,0.00003166347,0.9722058,0.0003095013,0.004954439,0.00051861,0.01972236],"study_design_scores_gemma":[0.000001699144,0.000005441085,0.0001775977,0.000004491334,0.000003623511,0.00000982982,0.000002920591,0.9965931,0.0001152077,0.00300482,0.00007805349,0.000003257512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06959598,0.0009223146,0.9256698,0.00073374,0.00006907313,0.00006236215,0.0001891435,0.0006607414,0.002096866],"genre_scores_gemma":[0.9140829,0.0004654742,0.0824321,0.0003550058,0.00006642886,0.00009042918,0.0004448379,0.00007543394,0.001987298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385978,"threshold_uncertainty_score":0.02755821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009879900757652745,"score_gpt":0.2903560433886933,"score_spread":0.2804761426310405,"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."}}