{"id":"W102130764","doi":"","title":"Developing Collision Prediction Models with Weather and Driver Characteristics Related Variables","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thursday; Collision; Names of the days of the week; Snow; Multinomial logistic regression; Daylight; Statistics; Collision frequency; Demography; Meteorology; Geography; Mathematics; Computer science; Computer security; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003701953,0.0004302031,0.000474487,0.0008700288,0.0008850194,0.0001348379,0.000309547,0.0004399452,0.0001221337],"category_scores_gemma":[0.00006186971,0.0003923821,0.00008476077,0.001703213,0.0006035757,0.001688037,0.00001201028,0.001525108,0.00005531164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000282387,"about_ca_system_score_gemma":0.0002512918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006165905,"about_ca_topic_score_gemma":0.0007712099,"domain_scores_codex":[0.9938499,0.0004364976,0.0009647541,0.000638325,0.002465202,0.001645341],"domain_scores_gemma":[0.9967217,0.00056263,0.00009726382,0.0003741875,0.001600347,0.0006438455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002247415,0.0006697548,0.677269,0.002647605,0.0009327456,0.0002013647,0.07723976,0.0959629,0.004556483,0.1199952,0.005303781,0.01297402],"study_design_scores_gemma":[0.00181128,0.0003167891,0.96548,0.000665519,0.00007720567,0.000003168864,0.00681247,0.0103227,0.0009425284,0.0009880341,0.01190526,0.0006750108],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797136,0.0005789796,0.0144587,0.0003603039,0.0002777355,0.00131002,0.0004974586,0.0006675261,0.002135739],"genre_scores_gemma":[0.9891914,0.002110448,0.006937105,0.00001814912,0.000193128,0.0002673601,0.0006349338,0.0001554775,0.000492068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2882111,"threshold_uncertainty_score":0.9998528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04061907884714777,"score_gpt":0.2995827284703945,"score_spread":0.2589636496232467,"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."}}