{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001948533,0.0002797575,0.0003604987,0.0005745374,0.00004991384,0.00001749348,0.0002425869,0.0001397576,0.00006487995],"category_scores_gemma":[0.00002737381,0.0002062572,0.0002207981,0.0002779218,0.00006534316,0.0001873378,0.00005142019,0.0001517034,0.000006219151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002656591,"about_ca_system_score_gemma":0.00002840549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003510971,"about_ca_topic_score_gemma":0.003672567,"domain_scores_codex":[0.9986547,0.00006244959,0.0004939457,0.0002356876,0.0002175691,0.0003356219],"domain_scores_gemma":[0.9989074,0.00008582096,0.0001207068,0.0006723763,0.000068977,0.0001447108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007093871,0.01178103,0.1005149,0.0002841505,0.00372647,0.002840547,0.006437523,0.2331538,0.0821497,0.005208577,0.2233902,0.3298037],"study_design_scores_gemma":[0.01313027,0.01387386,0.7140952,0.001028708,0.0008207617,0.002376496,0.005092753,0.2104847,0.03422094,0.0005318438,0.001545526,0.002798847],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9310504,0.00005770046,0.06336552,0.00007924305,0.00008479856,0.0008512575,0.000141184,0.003091493,0.001278422],"genre_scores_gemma":[0.9986492,0.00007438578,0.0006551642,0.00003719551,0.00004001588,0.0002936673,0.000004561477,0.00006630756,0.0001794391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6135803,"threshold_uncertainty_score":0.8410926,"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."}}