{"id":"W3086680895","doi":"10.1007/s10653-020-00708-x","title":"Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China","year":2020,"lang":"en","type":"article","venue":"Environmental Geochemistry and Health","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Urban agglomeration; China; Variation (astronomy); Class (philosophy); Environmental science; Geography; Economic geography; Physical geography; Computer science; Artificial intelligence; Archaeology","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":[0.0003897807,0.000452642,0.0003267718,0.001369436,0.0006045947,0.0007387213,0.0006983342,0.0004898573,0.0008572388],"category_scores_gemma":[0.0005012388,0.0003541341,0.000711786,0.00143293,0.0004062175,0.0005273638,0.0006976523,0.0002495554,0.0001837863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419448,"about_ca_system_score_gemma":0.001130664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1637945,"about_ca_topic_score_gemma":0.1668628,"domain_scores_codex":[0.9997191,0.00002860759,0.00002424667,0.00008626979,0.00005008594,0.00009178591],"domain_scores_gemma":[0.9995253,0.00007533754,0.000106616,0.00003939362,0.000119761,0.0001336054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001134828,0.00006149692,0.9892931,0.00001745009,0.0001253454,0.0002342551,0.0003935411,0.004703109,0.001244748,0.000175476,0.0004897012,0.003148346],"study_design_scores_gemma":[0.000003694813,0.00001236355,0.9885189,0.000002358549,0.00002085994,0.00002625641,0.0003321414,0.01070283,0.0001188423,0.00003379705,0.0002199723,0.000007870511],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994066,0.00002876959,0.00008342139,0.00002666132,0.000003104145,0.000002359292,0.0002866157,0.000008761336,0.0001537458],"genre_scores_gemma":[0.9990171,0.00002016387,0.00005926599,0.000005132275,0.00000388347,0.000004070067,0.0005828712,0.000002229802,0.0003051795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1637945,"threshold_uncertainty_score":0.3256823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085302441866619,"score_gpt":0.2364735993848863,"score_spread":0.2156205749662201,"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."}}