{"id":"W2046844036","doi":"10.1080/15389588.2012.743125","title":"Crash Involvement of Motor Vehicles in Relationship to the Number and Severity of Traffic Offenses. An Exploratory Analysis of Dutch Traffic Offenses and Crash Data","year":2012,"lang":"en","type":"article","venue":"Traffic Injury Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crash; Poison control; Speed limit; Transport engineering; Injury prevention; Engineering; Computer security; Statistics; Computer science; Medicine; Environmental health; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001749819,0.000277496,0.000358433,0.002799537,0.0001806419,0.0004428327,0.0003866789,0.0002872956,0.001652325],"category_scores_gemma":[0.009060328,0.0002989299,0.0007708098,0.002082155,0.0002622218,0.0005092288,0.0005924911,0.0002555333,0.0001897353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006048878,"about_ca_system_score_gemma":0.0003938499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01988768,"about_ca_topic_score_gemma":0.01824482,"domain_scores_codex":[0.9982955,0.0007057393,0.0002172478,0.0002730175,0.0003669507,0.000141579],"domain_scores_gemma":[0.9921548,0.004140115,0.002452895,0.0002791508,0.000699904,0.0002731767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001787066,0.0000218788,0.9964136,0.00004026876,0.000210136,0.0001605705,0.0002808992,0.0002774586,0.0004033116,0.00003576352,0.00009320433,0.001884124],"study_design_scores_gemma":[0.000002799669,0.00004824837,0.9987063,0.000005145919,0.00002341782,0.0001439545,0.0002564059,0.0005860127,0.00007777664,0.00001420399,0.0001316455,0.000004021497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974553,0.0001611498,0.0002924532,0.00001286933,0.000001056197,0.00002324882,0.001761171,0.000003765846,0.0002889468],"genre_scores_gemma":[0.9977927,0.00005082213,0.0002120728,0.00000257373,0.000001093602,0.00003295012,0.001793424,0.000004054099,0.0001102821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01988768,"threshold_uncertainty_score":0.03954387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04273328831167503,"score_gpt":0.2843461894973016,"score_spread":0.2416129011856265,"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."}}