{"id":"W3036496539","doi":"10.1016/j.scitotenv.2020.140264","title":"Contribution of urbanization to the changes in extreme climate events in urban agglomerations across China","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":146,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Natural Science Foundation of Shandong Province; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Urbanization; Urban agglomeration; Precipitation; China; Climate change; Urban climate; Geography; Climatology; Environmental science; Extreme weather; Physical geography; Economic geography; Meteorology; Ecology","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":[0.0009470349,0.00009331194,0.000109514,0.00002162798,0.0002351585,0.00001000351,0.0005838485,0.00002448607,0.0001217474],"category_scores_gemma":[0.0001426873,0.00005154498,0.00003110661,0.0008028295,0.0004978667,0.0001700649,0.0005512835,0.00009643921,0.00005689547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002430628,"about_ca_system_score_gemma":0.00001097115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001959429,"about_ca_topic_score_gemma":0.000138128,"domain_scores_codex":[0.9985697,0.0001055474,0.0002512433,0.0002328797,0.0005574109,0.0002832476],"domain_scores_gemma":[0.9994534,0.00002816067,0.0001321764,0.0003310068,0.000004565494,0.00005064602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005087,0.0001637271,0.08448153,0.000007574305,0.000004076308,3.343469e-7,0.01961241,0.425673,0.4682968,0.0005272839,0.0001974642,0.0009849612],"study_design_scores_gemma":[0.0002362874,0.00007971351,0.9353098,0.00002146476,0.0000059946,0.000001130821,0.0002448694,0.01154053,0.05224327,0.0001725519,0.00007146919,0.00007290756],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891487,0.00002555676,0.0001043213,0.009776283,0.00006799657,0.000683315,0.00002126981,0.000004469269,0.0001680614],"genre_scores_gemma":[0.9997028,0.00002236257,0.00004434212,0.0001005691,0.0000206524,0.00003057138,0.000002594846,0.000005074272,0.00007099809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8508283,"threshold_uncertainty_score":0.2101943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732499236241275,"score_gpt":0.2191771158249192,"score_spread":0.2018521234625065,"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."}}