{"id":"W2345406866","doi":"","title":"Impact of Weather Conditions on Traffic: Case Study of Montreal’s Winter","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Visibility; Metropolitan area; Meteorology; Snow; Traffic congestion; Environmental science; Automatic weather station; Geography; Air quality index; Transport engineering; Climatology; Engineering","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":[0.0004512896,0.0009121052,0.0002881399,0.0008774892,0.001503755,0.0009377,0.001419831,0.0008140795,0.001748405],"category_scores_gemma":[0.0009663445,0.0002087169,0.0005229202,0.001625763,0.0007448103,0.0003597272,0.0004723122,0.0005517874,0.0001392843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01010252,"about_ca_system_score_gemma":0.003119644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.868164,"about_ca_topic_score_gemma":0.9224163,"domain_scores_codex":[0.9996783,0.00006887382,0.00001038072,0.00005335187,0.00007100768,0.0001182009],"domain_scores_gemma":[0.999447,0.0001934116,0.00006786681,0.00003379336,0.0001301584,0.0001276645],"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":[0.0008265296,0.002375336,0.438655,0.0003605412,0.0004940852,0.03420881,0.002958865,0.4433186,0.0152686,0.004807637,0.01160964,0.04511626],"study_design_scores_gemma":[0.0001702917,0.001052315,0.6063524,0.00005803262,0.0002145214,0.001045602,0.008971975,0.3663406,0.005061125,0.0009382814,0.009613253,0.0001815292],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952166,0.00008980739,0.0007839025,0.000144934,0.00001136954,0.00008699807,0.0008873373,0.00005347838,0.002725523],"genre_scores_gemma":[0.9961082,0.0001182522,0.0009556328,0.00002944888,0.000009413758,0.00002825244,0.0005998246,0.00001138708,0.002139551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.131836,"threshold_uncertainty_score":0.2652248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009438691353919861,"score_gpt":0.242884943784267,"score_spread":0.2334462524303471,"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."}}