{"id":"W1820980500","doi":"10.1007/s40534-015-0073-3","title":"Using microscopic video data measures for driver behavior analysis during adverse winter weather: opportunities and challenges","year":2015,"lang":"en","type":"article","venue":"Journal of Modern Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Polytechnique Montréal; McGill University","funders":"","keywords":"Adverse weather; Crash; Collision; Environmental science; Transport engineering; Weather station; Computer science; Engineering; Meteorology; Geography","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":[0.002995118,0.0009986878,0.0007309595,0.002825931,0.0005207026,0.002108817,0.001280486,0.0006290816,0.0006847699],"category_scores_gemma":[0.007714839,0.0003653324,0.0004768767,0.001851965,0.0006148407,0.001559254,0.000618801,0.0006414011,0.0002395169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091075,"about_ca_system_score_gemma":0.001613592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0333635,"about_ca_topic_score_gemma":0.06420004,"domain_scores_codex":[0.997407,0.0008565386,0.0001738276,0.0003268179,0.001087593,0.0001482087],"domain_scores_gemma":[0.9899595,0.003384863,0.001533636,0.000969725,0.003905535,0.0002466855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003526022,0.0005701844,0.4716407,0.001511521,0.000545503,0.0003099667,0.001964416,0.02409989,0.04130894,0.003363323,0.004259283,0.4500737],"study_design_scores_gemma":[0.00007475208,0.001270318,0.6818853,0.0008320314,0.0004150496,0.0008433728,0.01047779,0.2260983,0.04514893,0.008424182,0.02416556,0.0003644708],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6542218,0.004006598,0.3212687,0.001474377,0.0004188566,0.0008892472,0.003395679,0.001059794,0.01326499],"genre_scores_gemma":[0.8888487,0.001900962,0.1065465,0.0001865874,0.0001270236,0.0002910372,0.001030164,0.00007875752,0.0009902606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0333635,"threshold_uncertainty_score":0.0663386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1730624912072958,"score_gpt":0.2916771509029646,"score_spread":0.1186146596956688,"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."}}