{"id":"W4401257062","doi":"10.1021/acs.est.4c03725","title":"Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression","year":2024,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; University of Victoria; Environment and Climate Change Canada; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada","keywords":"Downscaling; Environmental science; Chemical transport model; Air pollution; Pollutant; Truck; Regression analysis; Meteorology; Computer science; Air quality index; Engineering; Geography; Machine learning","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":[0.001245201,0.000598357,0.0003811774,0.0003771567,0.0002819271,0.0004642564,0.000674347,0.0006062207,0.0007028077],"category_scores_gemma":[0.004089299,0.0002825007,0.0006730388,0.0004273877,0.0003283998,0.0008047958,0.0005976201,0.0006720207,0.00008827404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007399751,"about_ca_system_score_gemma":0.0008205539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06509811,"about_ca_topic_score_gemma":0.03856517,"domain_scores_codex":[0.9997161,0.0001288736,0.00002173003,0.00006393984,0.00003862859,0.00003071009],"domain_scores_gemma":[0.9989904,0.0005236331,0.0001218445,0.0001640443,0.0001594057,0.00004068609],"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":[0.00009457941,0.0001006397,0.02872237,0.00002238673,0.00008477525,0.00003285494,0.00005130354,0.9573756,0.002013154,0.0004948037,0.000144719,0.01086279],"study_design_scores_gemma":[0.000009642021,0.00002938358,0.00489215,0.000002859629,0.00000861821,0.000004544389,0.00002618175,0.9939184,0.0007969881,0.0001803331,0.0001245001,0.000006352368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971786,0.00006892011,0.02629949,0.0002050532,0.00002020636,0.0000594979,0.0004048185,0.0002288335,0.000927246],"genre_scores_gemma":[0.9767141,0.00003515845,0.02259987,0.00003761092,0.000007803758,0.00002725055,0.0003663028,0.00003070742,0.0001811359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06509811,"threshold_uncertainty_score":0.1294384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06285983082370158,"score_gpt":0.3157351365286953,"score_spread":0.2528753057049937,"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."}}