{"id":"W2969433991","doi":"10.1016/j.scitotenv.2019.133983","title":"Research on PM2.5 estimation and prediction method and changing characteristics analysis under long temporal and large spatial scale - A case study in China typical regions","year":2019,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Environmental science; Beijing; Particulates; Range (aeronautics); Delta; Air quality index; Pollution; China; Meteorology; Physical geography; Geography","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":[0.002071441,0.0007041365,0.0006444176,0.001255728,0.0007765897,0.001333877,0.001329443,0.0009528897,0.0006703716],"category_scores_gemma":[0.002450245,0.0004249429,0.001360905,0.001547623,0.0004491454,0.001649637,0.0006066221,0.0004524642,0.00009185355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767601,"about_ca_system_score_gemma":0.001750351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1219378,"about_ca_topic_score_gemma":0.07349753,"domain_scores_codex":[0.9990498,0.0002031014,0.00005811372,0.0002680394,0.0002309113,0.0001899874],"domain_scores_gemma":[0.9987469,0.0005590226,0.000129371,0.0001131199,0.0003792466,0.00007237199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003687803,0.0004764617,0.4346969,0.000472086,0.000485997,0.002358544,0.0009714426,0.4440206,0.009026016,0.00453032,0.002553049,0.1000398],"study_design_scores_gemma":[0.00001902353,0.00008009662,0.1442652,0.00002069191,0.0001961742,0.0001881252,0.0009633517,0.8489585,0.002942883,0.001095168,0.001226905,0.00004384829],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973475,0.0005787922,0.02338699,0.0004481955,0.0000356097,0.00005086185,0.0003041301,0.00007454173,0.001645852],"genre_scores_gemma":[0.9934121,0.000340326,0.005034455,0.0000229843,0.00003725025,0.00002260484,0.0003236594,0.00001169072,0.0007949887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1219378,"threshold_uncertainty_score":0.2424561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04910161303995447,"score_gpt":0.3666356997192466,"score_spread":0.3175340866792922,"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."}}