{"id":"W590735017","doi":"10.1016/j.rse.2015.05.016","title":"Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":286,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Collaborative Innovation Center for Regional Environmental Quality; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"Environmental science; Satellite; Aerosol; Chemical transport model; Particulates; Remote sensing; Lidar; Air quality index; Population; Meteorology; Atmospheric sciences; Geography; Environmental health; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007709325,0.001142274,0.0005071442,0.001055297,0.000593767,0.0006710789,0.0008191039,0.0008436772,0.0003553228],"category_scores_gemma":[0.0007589491,0.0007291367,0.0009295669,0.0009206056,0.0003213017,0.0008929307,0.000450987,0.0002828921,0.00009295489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003168802,"about_ca_system_score_gemma":0.002635333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.327644,"about_ca_topic_score_gemma":0.2307883,"domain_scores_codex":[0.9998267,0.00002136967,0.00001510244,0.00006291848,0.00003191152,0.00004191702],"domain_scores_gemma":[0.9996635,0.0001030529,0.00005243086,0.00003081842,0.00009942571,0.0000508109],"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":[0.0002096054,0.0002161642,0.187629,0.0001149389,0.0003671189,0.0002382123,0.00009681926,0.7823843,0.00831297,0.0004008722,0.0004948599,0.01953516],"study_design_scores_gemma":[0.00003668631,0.00004144519,0.09056161,0.000006462673,0.0001096817,0.00001458065,0.00005246718,0.9072558,0.001562435,0.0001575868,0.0001748212,0.00002646464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979622,0.0001258925,0.001282921,0.00004859065,0.000006027815,0.000006594506,0.0002512279,0.00005433416,0.0002621078],"genre_scores_gemma":[0.9983391,0.00008988087,0.0008457183,0.000008180176,0.000004926623,0.000007173131,0.0004642948,0.000004702029,0.0002361157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.327644,"threshold_uncertainty_score":0.6514738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0632619667945429,"score_gpt":0.3038773551882903,"score_spread":0.2406153883937474,"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."}}