{"id":"W2953356365","doi":"10.1016/j.jclepro.2019.05.389","title":"Investigating urban heat island through spatial analysis of New York City streetscapes","year":2019,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Faculty of Arts, Ryerson University; Binghamton University; Ryerson University","keywords":"Urban heat island; Mean radiant temperature; Land cover; Environmental science; Urbanization; Geographically Weighted Regression; Physical geography; Geography; Spatial ecology; Elevation (ballistics); Regression analysis; Land use; Meteorology; Climate change; Statistics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001123153,0.0001604939,0.0001937452,0.001852003,0.0005573974,0.0009252336,0.0002349568,0.0001651487,0.001879531],"category_scores_gemma":[0.0005290149,0.0001387766,0.0002818674,0.003814825,0.0002312534,0.0003601374,0.000539176,0.0001760913,0.0003047423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008554351,"about_ca_system_score_gemma":0.0007859381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4089049,"about_ca_topic_score_gemma":0.667263,"domain_scores_codex":[0.9998747,0.00002185117,0.000008069797,0.00002636318,0.00003860614,0.00003038194],"domain_scores_gemma":[0.9996623,0.0000691746,0.00005340793,0.00002918096,0.000148491,0.0000375193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001339264,0.00005985782,0.9696798,0.00005708569,0.0001342086,0.0002878411,0.00159647,0.00423402,0.004056199,0.0007799903,0.003489044,0.01549165],"study_design_scores_gemma":[0.000002357762,0.000009979341,0.990337,0.00000676975,0.00002376183,0.00004057096,0.002436129,0.004857577,0.0003741349,0.00004814953,0.001855369,0.000008276495],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934765,0.0000691135,0.000473439,0.00004157547,0.000006692998,0.00001438226,0.003205481,0.0000241985,0.002688649],"genre_scores_gemma":[0.9957754,0.00007158688,0.0009033541,0.000005266103,0.000005528015,0.00002505718,0.002258066,0.000009435361,0.0009463748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4089049,"threshold_uncertainty_score":0.8130496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116128790134734,"score_gpt":0.2332665200859702,"score_spread":0.2121052321846228,"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."}}