{"id":"W4362677654","doi":"10.1016/j.uclim.2023.101506","title":"Detecting local climate zone change and its effects on PM10 distribution using fuzzy machine learning in Tehran, Iran","year":2023,"lang":"en","type":"article","venue":"Urban Climate","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Athabasca University","funders":"","keywords":"Metropolitan area; Climate change; Land cover; Environmental science; Geography; Particulates; Air pollution; Physical geography; Climate zones; Land use; Geology","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":[0.000366623,0.0002992107,0.0002964058,0.0007744104,0.0004279346,0.0004079671,0.0004696184,0.0003013349,0.0004445327],"category_scores_gemma":[0.0004702817,0.0001303859,0.0003835032,0.0007134176,0.0001928167,0.0003204708,0.0002630133,0.0002434872,0.00009266753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000853318,"about_ca_system_score_gemma":0.0008564763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09478604,"about_ca_topic_score_gemma":0.1000185,"domain_scores_codex":[0.999841,0.00002738057,0.00001275728,0.00003763065,0.00003523885,0.0000459666],"domain_scores_gemma":[0.99978,0.00005660755,0.00003884518,0.00001017767,0.00008795957,0.00002636143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002874084,0.0003804998,0.924307,0.000104983,0.0001902629,0.0004720981,0.0005500731,0.02836744,0.003640048,0.0004670354,0.001877086,0.03935605],"study_design_scores_gemma":[0.0000262437,0.0001011284,0.9183937,0.00001267549,0.000107209,0.0001156723,0.002421462,0.07604735,0.001550536,0.0003680609,0.000832828,0.00002301756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983447,0.00006556913,0.0006906209,0.00007892269,0.000009224732,0.000006863867,0.0002640302,0.00001941524,0.0005206221],"genre_scores_gemma":[0.9989161,0.00003275771,0.0005002349,0.000007509409,0.000007600508,0.000005981903,0.000255812,0.00000148022,0.0002724224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09478604,"threshold_uncertainty_score":0.1884686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03504837023044678,"score_gpt":0.2521877966136443,"score_spread":0.2171394263831975,"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."}}