{"id":"W4362686345","doi":"10.3808/jeil.202300100","title":"Regional PM2.5 Estimation for Southern Ontario through Geographically Weighted Regression","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Informatics Letters","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Geographically Weighted Regression; Environmental science; Satellite; Ordinary least squares; Estimation; Regression analysis; Inversion (geology); Particulates; Meteorology; Regression; Climatology; Geography; Statistics; Geology; Mathematics","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":[0.0002622841,0.000373568,0.0001725643,0.0004780678,0.0002749072,0.0003690369,0.0004422619,0.0001767576,0.0006365132],"category_scores_gemma":[0.0008868005,0.000159384,0.0002755595,0.0006394577,0.0001128864,0.0002829681,0.0002343482,0.000141768,0.0001292162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00253941,"about_ca_system_score_gemma":0.002693403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8294831,"about_ca_topic_score_gemma":0.8657948,"domain_scores_codex":[0.9998777,0.00001434546,0.000006554014,0.00004880059,0.00003348713,0.00001905871],"domain_scores_gemma":[0.9998471,0.00002006787,0.00003154074,0.000009009905,0.0000820425,0.0000103026],"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":[0.0001252079,0.00003659003,0.3124492,0.0001053401,0.0001274132,0.0003154347,0.0002824173,0.6122561,0.008808301,0.001264079,0.001736958,0.06249301],"study_design_scores_gemma":[0.00001025983,0.00001716157,0.1283038,0.000008848152,0.00003357014,0.00002531191,0.0002135262,0.8679909,0.00132889,0.0003249967,0.001723686,0.00001918967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9675552,0.0002387527,0.02727488,0.0001442133,0.0000102922,0.00004373132,0.00162285,0.0002700875,0.002840028],"genre_scores_gemma":[0.9902976,0.0000813449,0.007652331,0.00000655744,0.000003076481,0.00001110384,0.0008322429,0.00001329784,0.001102472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1705169,"threshold_uncertainty_score":0.3430422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03305276949629,"score_gpt":0.2777063199812704,"score_spread":0.2446535504849804,"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."}}