{"id":"W2123768945","doi":"10.1016/j.atmosenv.2011.07.068","title":"Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010","year":2011,"lang":"en","type":"article","venue":"Atmospheric Environment","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":183,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"AERONET; Environmental science; Aerosol; Particulates; Satellite; Biomass burning; Atmospheric sciences; Meteorology; Pollution; Climatology; Air pollution; Geography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001283486,0.0001954902,0.0002516298,0.000001023099,0.00007612937,0.000005624881,0.0002284061,0.00005393112,0.004552583],"category_scores_gemma":[0.00001148586,0.0001371472,0.00006281037,0.0001702648,0.0001292769,0.00009225809,0.0000429028,0.0001107242,0.00003323762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000132084,"about_ca_system_score_gemma":0.00001541582,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0107679,"about_ca_topic_score_gemma":0.001084412,"domain_scores_codex":[0.9986907,0.00006626311,0.0004500405,0.0002798073,0.0002494971,0.0002637451],"domain_scores_gemma":[0.9991986,0.00007323672,0.0002193004,0.0004364885,0.000006815527,0.00006562299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005356789,0.0004428511,0.9757289,0.00004611667,0.00002836761,0.0001633732,0.001353267,0.0207629,0.0006259948,1.183909e-7,0.000003385215,0.0007911794],"study_design_scores_gemma":[0.0007267173,0.000133005,0.9905466,0.00004213981,0.00003844717,0.00004820658,0.0008842009,0.002991853,0.00439039,0.00003849093,0.000006861859,0.0001531038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991205,0.000270272,0.00006723839,0.00002639421,0.00006568994,0.0003365115,0.00001219233,0.000007645654,0.00009353493],"genre_scores_gemma":[0.9944604,0.00001616383,0.005268139,0.0000208391,0.000009828469,0.000009395965,0.000004563318,0.000007166235,0.000203481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01777105,"threshold_uncertainty_score":0.9963574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03399327673757067,"score_gpt":0.195807330486092,"score_spread":0.1618140537485213,"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."}}