{"id":"W2404788231","doi":"10.5194/isprs-archives-xli-b2-721-2016","title":"ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Chinese Academy of Sciences","keywords":"Environmental science; Beijing; Moderate-resolution imaging spectroradiometer; Particulates; Megacity; China; Air pollution; Population; Meteorology; Pollution; Estimation; Aerosol; Satellite; Geography; Physical geography; Climatology; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005061995,0.0006372195,0.0002843683,0.001076835,0.0003464007,0.0004876277,0.0004990584,0.0003484722,0.0003294337],"category_scores_gemma":[0.0004302897,0.0002444299,0.000610622,0.001025353,0.0001799513,0.0006593783,0.0003558607,0.0001992314,0.0001366179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009604339,"about_ca_system_score_gemma":0.000759857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1256893,"about_ca_topic_score_gemma":0.1351427,"domain_scores_codex":[0.9997641,0.00002472631,0.00002469191,0.00009794688,0.00006011076,0.00002847819],"domain_scores_gemma":[0.9998216,0.00001635303,0.00003451756,0.00001705472,0.0000905675,0.00001991938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002300532,0.0001350621,0.8975792,0.0001919851,0.000505522,0.0003349106,0.0003466246,0.04919446,0.01183378,0.0002635252,0.002211779,0.03717312],"study_design_scores_gemma":[0.00001331246,0.00004100334,0.9049175,0.00002012899,0.0001481782,0.00006504791,0.0003962163,0.08893636,0.004024193,0.0001001763,0.001307737,0.00003010294],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953068,0.0002735154,0.00184271,0.00004233685,0.00001306753,0.00001680698,0.001751817,0.00006528301,0.0006877417],"genre_scores_gemma":[0.9946912,0.0001239312,0.002145392,0.00001209485,0.000005961996,0.00001502452,0.002623982,0.000007889884,0.0003745806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1256893,"threshold_uncertainty_score":0.2499155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03396324927954966,"score_gpt":0.2940675296729525,"score_spread":0.2601042803934028,"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."}}