{"id":"W4289333323","doi":"10.1061/jtepbs.0000738","title":"Impact of COVID-19 on Traffic Volume, Violations, and Crashes in Fortaleza, Brazil","year":2022,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crash; Poisson regression; Social isolation; Negative binomial distribution; Poison control; Medicine; Coronavirus disease 2019 (COVID-19); Traffic volume; Injury prevention; Poisson distribution; Environmental health; Volume (thermodynamics); Demography; Transport engineering; Statistics; Computer science; Engineering; Internal medicine; Mathematics; Psychiatry","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":[0.001267214,0.000142198,0.0005498151,0.000269617,0.00006765103,0.000008809443,0.0001036716,0.00004544426,0.00004495028],"category_scores_gemma":[0.0007954027,0.0001181524,0.0001849187,0.0002693475,0.00001894433,0.00008745673,0.000004558356,0.000245742,2.789994e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002696788,"about_ca_system_score_gemma":0.0000921568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009173989,"about_ca_topic_score_gemma":0.00005278752,"domain_scores_codex":[0.9982449,0.0000979351,0.001068238,0.0001212419,0.0003128557,0.0001547999],"domain_scores_gemma":[0.998072,0.00111525,0.0005425814,0.0000978789,0.00005848336,0.000113832],"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":[0.00006394109,0.00009957534,0.1438651,0.0003326086,0.0000964344,0.00002410921,0.001158487,0.8525742,0.00010263,0.0005387646,0.001101275,0.00004293375],"study_design_scores_gemma":[0.002215131,0.001477354,0.9180806,0.0003545802,0.0001482297,0.00006927299,0.00108423,0.06694333,0.000008647714,0.0004528367,0.008790547,0.0003752248],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900785,0.0006229541,0.008546854,0.0001902317,0.0002154146,0.000233916,0.00008057749,0.00002601475,0.000005524429],"genre_scores_gemma":[0.9995105,0.00005469234,0.0002898528,0.00001965948,0.0000512797,0.00002985044,0.000007590331,0.00001543748,0.00002108451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7856308,"threshold_uncertainty_score":0.4818115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08993551140218531,"score_gpt":0.3758778903393977,"score_spread":0.2859423789372124,"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."}}