{"id":"W2923954481","doi":"10.3390/ijerph16071099","title":"Spatiotemporal Pattern of Fine Particulate Matter and Impact of Urban Socioeconomic Factors in China","year":2019,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Dalhousie University; National Natural Science Foundation of China","keywords":"China; Urbanization; Pollution; Air pollution; Particulates; Environmental science; Geography; Air quality index; Haze; Socioeconomic status; Driving factors; Land use; Environmental protection; Physical geography; Environmental health; Meteorology; Ecology; Population","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":[0.0005999492,0.0003100738,0.0003344342,0.001067257,0.0003970488,0.0004990249,0.0003238014,0.0002618109,0.0009056553],"category_scores_gemma":[0.0007318729,0.0002336982,0.0006314958,0.001952549,0.0003005596,0.0003534731,0.0006129818,0.0002375044,0.0001255582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009882253,"about_ca_system_score_gemma":0.001105003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07944012,"about_ca_topic_score_gemma":0.08653551,"domain_scores_codex":[0.9996343,0.00005048989,0.00004312608,0.0001118265,0.00007622284,0.00008399047],"domain_scores_gemma":[0.9993647,0.00007428134,0.0001581961,0.00006951557,0.0001753519,0.0001579448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000321979,0.00001413397,0.995144,0.00002795861,0.0001143676,0.0001308014,0.0002524996,0.0003832779,0.0005410072,0.0001485288,0.0002637255,0.002947571],"study_design_scores_gemma":[0.000001214215,0.000008215535,0.9993132,0.000002382815,0.00001042763,0.000020366,0.00007816897,0.0003542865,0.00002502909,0.00002083616,0.0001621972,0.000003534378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983302,0.0002624714,0.00008528044,0.00008721185,0.000005203474,0.00000689864,0.0007235159,0.000005701812,0.0004935872],"genre_scores_gemma":[0.9989587,0.0001029155,0.00006011047,0.0000183435,0.000004837736,0.000005702279,0.0006341624,0.000001564821,0.0002135856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07944012,"threshold_uncertainty_score":0.1579555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701846430695238,"score_gpt":0.3854765759900826,"score_spread":0.3284581116831302,"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."}}