{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006428446,0.0001984328,0.000201988,0.00009320348,0.0003117268,0.00004086114,0.00008105092,0.0001032976,0.00004283242],"category_scores_gemma":[0.00009700987,0.0001951353,0.00003677934,0.0005514315,0.0000539449,0.0002655825,0.0002013989,0.0002939395,0.0004243488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002739011,"about_ca_system_score_gemma":0.000002519254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002310426,"about_ca_topic_score_gemma":0.0003608524,"domain_scores_codex":[0.9983085,0.0001383129,0.0002424534,0.0004141763,0.000256969,0.0006396089],"domain_scores_gemma":[0.9995112,0.0001684523,0.00008885453,0.0001266585,0.000004847278,0.0001000281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001138705,0.00005133498,0.9209248,0.0002703236,0.000007305566,0.0001075512,0.001410078,0.002863444,0.04834932,0.0001161355,0.00004720153,0.02573861],"study_design_scores_gemma":[0.00153368,0.000334456,0.6473274,0.0004321823,0.00003576347,0.00002446937,0.00015797,0.3322312,0.01678922,0.00008415725,0.0005594594,0.0004901393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985828,0.0001223552,0.0001323936,0.00007117735,0.0001437959,0.0004123108,0.00004678533,0.00020735,0.0002809628],"genre_scores_gemma":[0.9994037,0.0001521887,0.00004681951,0.00007232765,0.00008320237,0.00003666459,0.0001329732,0.00003239502,0.00003969402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3293677,"threshold_uncertainty_score":0.7957386,"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."}}