{"id":"W1551496553","doi":"10.1016/j.aap.2014.03.012","title":"Methodology to develop crash modification functions for road safety treatments with fully specified and hierarchical models","year":2014,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Categorical variable; Crash; Function (biology); Computer science; Multiplicative function; Hierarchical database model; Influence function; Engineering; Econometrics; Data mining; Mathematics; Statistics; Machine learning","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":[0.000391637,0.0001557635,0.0002801715,0.0003032262,0.0001264624,0.00004255485,0.00008134313,0.00007507066,0.00001809608],"category_scores_gemma":[0.00005172349,0.0001344811,0.0000931117,0.0005852412,0.00001301959,0.0001981811,0.00002290484,0.00007445184,0.000004965962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008302291,"about_ca_system_score_gemma":0.00001281616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002727375,"about_ca_topic_score_gemma":0.0007263345,"domain_scores_codex":[0.9990113,0.00008239902,0.000288327,0.0002790704,0.0001213278,0.0002175358],"domain_scores_gemma":[0.9994314,0.00007518409,0.00005867625,0.000218748,0.0001389828,0.0000769567],"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":[0.0002957972,0.0000423065,0.0278021,0.00001256716,0.003116927,7.288667e-7,0.0007536805,0.6403499,0.004253579,0.00489051,0.0002237414,0.3182581],"study_design_scores_gemma":[0.001340613,0.0002589648,0.7208511,0.00004383719,0.003791181,0.000006286416,0.0002109278,0.2573301,0.002698297,0.01150158,0.001475875,0.0004911239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2878426,0.0000227355,0.7113436,0.00005711512,0.00009191384,0.0002458807,6.919514e-7,0.00006241814,0.0003330146],"genre_scores_gemma":[0.9241253,0.0000299111,0.0749331,0.00001553677,0.0001289808,0.0001425707,0.0001329088,0.00001859527,0.0004730934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6930491,"threshold_uncertainty_score":0.5483982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03884183952183101,"score_gpt":0.2901174038073717,"score_spread":0.2512755642855407,"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."}}