{"id":"W4393719803","doi":"10.5281/zenodo.5748416","title":"A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motor carrier; Actuarial science; Business; Motor vehicle crash; Computer security; Forensic engineering; Transport engineering; Engineering; Database; Human factors and ergonomics; Poison control; Environmental health; Computer science; Medicine; Truck; Automotive engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00103637,0.001167497,0.001054159,0.003110725,0.000529971,0.001179938,0.001925239,0.001437736,0.04404235],"category_scores_gemma":[0.006121055,0.0004820703,0.0008820906,0.005051268,0.0002013815,0.0007956264,0.001350609,0.001448405,0.05246847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143984,"about_ca_system_score_gemma":0.002354549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01658798,"about_ca_topic_score_gemma":0.0272024,"domain_scores_codex":[0.9989508,0.0001800773,0.0001640877,0.0002838222,0.0003001298,0.0001210909],"domain_scores_gemma":[0.9969555,0.0007411637,0.0004346896,0.0005531649,0.001001709,0.000313654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006504419,0.00005111291,0.002811939,0.0003242442,0.00003295795,0.00003013256,0.00001854406,0.0004138129,0.00009432427,0.0004244145,0.9908395,0.004894025],"study_design_scores_gemma":[0.0002499734,0.00005806702,0.03647794,0.0005272468,0.00006692676,0.0001903425,0.0001661768,0.002190354,0.0006197816,0.001756408,0.9576301,0.00006668409],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005412178,0.00005700494,0.0001907544,0.00006411519,0.00003077146,0.00001792499,0.9981313,0.0002628472,0.0007040279],"genre_scores_gemma":[0.0008360158,0.00004456719,0.000291115,0.00002823496,0.000009964532,0.00007688015,0.9981717,0.00003343567,0.0005080492],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04404235,"threshold_uncertainty_score":0.1473364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03170651620115968,"score_gpt":0.2608307523873226,"score_spread":0.2291242361861629,"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."}}