{"id":"W2947380028","doi":"10.1016/j.envres.2019.05.044","title":"Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"Environmental Research","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ministry of Economy; Cancer Research Society","keywords":"Convolutional neural network; Satellite; Scale (ratio); Environmental science; Regression analysis; Satellite imagery; Remote sensing; Regression; Spatial ecology; Artificial neural network; Meteorology; Deep learning; Computer science; Statistics; Cartography; Artificial intelligence; Geography; Machine learning; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0003307362,0.0005262739,0.0002938437,0.0004861516,0.0001740403,0.0005855581,0.0006437592,0.0004829337,0.001166306],"category_scores_gemma":[0.001078564,0.0002944112,0.0006447757,0.000561566,0.0002226092,0.001009995,0.0006407535,0.0008055254,0.0004660446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005639163,"about_ca_system_score_gemma":0.0004784897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04645315,"about_ca_topic_score_gemma":0.04362492,"domain_scores_codex":[0.9999176,0.00001111056,0.000003112778,0.00003977431,0.0000132025,0.00001509428],"domain_scores_gemma":[0.999787,0.00007230096,0.00003233135,0.00003940547,0.00005392694,0.00001512516],"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":[0.0001389685,0.0001546651,0.02533088,0.00007858418,0.0002673071,0.000126817,0.00006274725,0.8368659,0.01068673,0.004583901,0.004364339,0.1173391],"study_design_scores_gemma":[0.000003106893,0.000004140793,0.003777671,0.000003281684,0.00001023043,0.000007232719,0.000006666792,0.9937781,0.0004772782,0.001553147,0.000374715,0.000004491309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6351684,0.001791184,0.3501567,0.001053688,0.0002449765,0.00003485703,0.002561752,0.001898885,0.007089647],"genre_scores_gemma":[0.9767861,0.0003892035,0.0187524,0.0001046237,0.00007073901,0.00001429531,0.001091611,0.00009427079,0.002696631],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04645315,"threshold_uncertainty_score":0.0923655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1059986014297365,"score_gpt":0.3668142495226676,"score_spread":0.2608156480929311,"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."}}