{"id":"W4406198411","doi":"10.1016/j.uclim.2024.102263","title":"Impact of climatic parameters on spatiotemporal variation of air pollutants across Bangladesh","year":2025,"lang":"en","type":"article","venue":"Urban Climate","topic":"COVID-19 impact on air quality","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"","keywords":"Variation (astronomy); Pollutant; Environmental science; Air pollutants; Air pollution; Atmospheric sciences; Climate change; Physical geography; Climatology; Geography; Ecology; Geology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001154717,0.0001875497,0.0003490857,0.00007744738,0.00007596173,0.00001787049,0.0002927085,0.0001007539,0.0005943959],"category_scores_gemma":[0.000487886,0.000163926,0.0002071484,0.0005341707,0.0002017588,0.0001976195,0.0001870887,0.0001228241,0.0001034123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005651665,"about_ca_system_score_gemma":0.0000471276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00138031,"about_ca_topic_score_gemma":0.0000877604,"domain_scores_codex":[0.9980556,0.0002050708,0.0006275204,0.0002930943,0.0004040036,0.0004147601],"domain_scores_gemma":[0.9985253,0.0003771799,0.0004419776,0.0005496808,0.00001416958,0.00009166767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003011196,0.0004073359,0.9713919,0.0001864488,0.00006616469,0.000002128926,0.002289779,0.005191241,0.01469889,0.0002488501,0.0006131197,0.004603007],"study_design_scores_gemma":[0.000613881,0.0002774635,0.9897937,0.00009136005,0.0000229565,4.236895e-7,0.00006152492,0.005298917,0.003116724,0.0005628692,0.00002456091,0.0001356822],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966871,0.000006625036,0.0004313375,0.0002583784,0.0001128192,0.0003224736,0.0004660191,0.0000412044,0.001674043],"genre_scores_gemma":[0.9989855,0.000007071971,0.0005230373,0.0003773972,0.000006494267,0.000005912467,0.000010857,0.00001304422,0.00007073518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01840173,"threshold_uncertainty_score":0.6684706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549288265162997,"score_gpt":0.3528736816911239,"score_spread":0.327380799039494,"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."}}