{"id":"W4312141395","doi":"10.21926/jept.2204043","title":"Traffic NO<sub>x</sub> Pollution Prediction and Health Cost Estimation Using Machine Learning: A Case Study of Toronto, Canada","year":2022,"lang":"en","type":"article","venue":"Journal of Energy and Power Technology","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Software deployment; Air pollution; Human health; Environmental science; Pollution; Fuel efficiency; Transport engineering; Environmental economics; Environmental health; Meteorology; Geography; Engineering; Automotive engineering; Medicine; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006117437,0.0009429542,0.0004032172,0.0007558636,0.00147639,0.0009781339,0.001262625,0.001028452,0.001463719],"category_scores_gemma":[0.001466399,0.0002796401,0.0007463436,0.001551696,0.0008559174,0.0004160086,0.0005194318,0.0006942274,0.0001507569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02452237,"about_ca_system_score_gemma":0.01479109,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9809877,"about_ca_topic_score_gemma":0.9759861,"domain_scores_codex":[0.999557,0.00007604358,0.0000223599,0.00007791686,0.0001175064,0.0001491328],"domain_scores_gemma":[0.9991128,0.0003413667,0.00005487219,0.00003627844,0.0003544657,0.0001002183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000410244,0.0004251277,0.1101706,0.0002838571,0.0001711971,0.004009516,0.0005188984,0.8451756,0.001950301,0.003771541,0.00584765,0.02726543],"study_design_scores_gemma":[0.00005706722,0.0001039161,0.06006867,0.0000461219,0.0000870607,0.000138471,0.001940303,0.932754,0.001567321,0.0006697407,0.002509266,0.00005791887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847882,0.0005694938,0.005471185,0.001225584,0.00002464646,0.0001460291,0.002274598,0.0001073593,0.005392978],"genre_scores_gemma":[0.9935983,0.0003691327,0.002623408,0.00004783595,0.000007094166,0.00002946873,0.001010462,0.00001070086,0.002303595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02452237,"threshold_uncertainty_score":0.177923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007874977075656299,"score_gpt":0.2234998525041442,"score_spread":0.2156248754284879,"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."}}