{"id":"W4312141395","doi":"10.21926/jept.2204043","title":"Traffic NO&lt;sub&gt;x&lt;/sub&gt; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002493946,0.00009942252,0.0002122643,0.000123399,0.0002548105,0.000006702689,0.00005499643,0.00006594953,0.00002698127],"category_scores_gemma":[0.00001391658,0.00009606637,0.0000167955,0.0001523682,0.00002173124,0.000115337,0.00003622048,0.0002612952,1.942712e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002821793,"about_ca_system_score_gemma":0.0001152025,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01514054,"about_ca_topic_score_gemma":0.1135696,"domain_scores_codex":[0.9992233,0.00004417418,0.0003597495,0.00008601808,0.0001435449,0.000143227],"domain_scores_gemma":[0.999584,0.00001341745,0.0002027088,0.00007896132,0.00005275196,0.00006812929],"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":[0.00007787986,0.0001536,0.00314776,0.00005051551,0.000103733,0.0002488977,0.0006322629,0.9143198,0.01186581,0.0001313893,0.0006389253,0.06862948],"study_design_scores_gemma":[0.001387223,0.002205926,0.001600128,0.00005640782,0.00004205168,0.009622852,0.001263207,0.9622477,0.0008201011,0.00001077252,0.02058171,0.0001618904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941775,0.00450109,0.0007033642,0.0001619777,0.0003181082,0.0000644328,0.00001197122,0.00003352477,0.00002801268],"genre_scores_gemma":[0.9990391,0.0007216679,0.0001645946,0.00001743836,0.0000270659,0.000003579248,0.000003126814,0.00001210238,0.00001129471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0984291,"threshold_uncertainty_score":0.9914177,"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."}}