{"id":"W4302012080","doi":"10.32920/ryerson.14661255.v1","title":"Air quality improvement in urban areas using traffic operation management","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pollutant; Particulates; Air quality index; Environmental science; Air pollution; Public transport; Air pollutants; Criteria air contaminants; Pollution; Business; Environmental planning; Transport engineering; Environmental engineering; Environmental protection; Natural resource economics; Engineering; Meteorology; Geography; Economics","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":[0.0003446079,0.0003138545,0.0001914762,0.0005786243,0.0004210132,0.000869244,0.0002387274,0.0003123019,0.002543651],"category_scores_gemma":[0.0003810254,0.00008009435,0.000220537,0.001240947,0.0002029686,0.0003812018,0.0004084781,0.0001463835,0.0005453572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003730333,"about_ca_system_score_gemma":0.0005550407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006393462,"about_ca_topic_score_gemma":0.004459338,"domain_scores_codex":[0.9997087,0.00008971043,0.00001071189,0.00004341556,0.00009231827,0.00005502759],"domain_scores_gemma":[0.9998794,0.00001820538,0.00002368719,0.00001540564,0.00004719236,0.00001610186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004001335,0.0009860856,0.06710694,0.0005811387,0.0001181155,0.0003794748,0.0005424664,0.122972,0.05138486,0.0113577,0.008879757,0.7352914],"study_design_scores_gemma":[0.0001240104,0.002084419,0.2710899,0.0001902234,0.000270401,0.0004911673,0.003148546,0.5495175,0.04449324,0.02439832,0.1040921,0.0001001886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8549626,0.002829659,0.09126895,0.0009129354,0.0001772431,0.0002590921,0.0006898529,0.001186389,0.04771334],"genre_scores_gemma":[0.9854932,0.000847018,0.00848976,0.00002174467,0.00004736571,0.00003820561,0.0003517029,0.00002844734,0.004682661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006393462,"threshold_uncertainty_score":0.01271248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02719132975759621,"score_gpt":0.2801566569527538,"score_spread":0.2529653271951576,"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."}}