{"id":"W4409433509","doi":"10.1016/j.enbuild.2025.115720","title":"Predicting long-term urban overheating and their Mitigations from nature based solutions using Machine learning and field measurements","year":2025,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; National Research Council Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Concordia University","keywords":"Overheating (electricity); Term (time); Environmental science; Computer science; Machine learning; Engineering; Artificial intelligence; Meteorology; Geography; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001329064,0.0001144001,0.00009765021,0.00004291435,0.0005913412,0.00006220477,0.00004193991,0.00011382,0.00006052292],"category_scores_gemma":[0.00009223249,0.0001033394,0.00001811893,0.0001162138,0.00007493105,0.0001766477,0.0001118317,0.0001802505,1.550614e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004517852,"about_ca_system_score_gemma":0.000009042493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001635932,"about_ca_topic_score_gemma":0.0007515832,"domain_scores_codex":[0.9993169,0.00004559927,0.0001249813,0.0002598675,0.0000922546,0.0001604036],"domain_scores_gemma":[0.999666,0.0001585818,0.00004851882,0.00006415503,0.000007666172,0.00005505347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005950448,0.000009873766,0.9226613,0.000007325492,0.00001582503,5.355309e-7,0.0001981622,0.0001618438,0.07369314,0.0001180228,0.0000454182,0.003082595],"study_design_scores_gemma":[0.001555957,0.0001099443,0.6586676,0.0007726651,0.0001532334,0.00001226439,0.0002194237,0.1898565,0.1440478,0.001809433,0.002265281,0.0005298547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900935,0.001450349,0.007379075,0.0002378589,0.00006407374,0.00005457881,0.000008107199,0.00003604109,0.0006764237],"genre_scores_gemma":[0.9977093,0.00004031185,0.00165972,0.0003557102,0.00003631836,0.000005215678,0.00002171083,0.000006968774,0.0001647976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2639937,"threshold_uncertainty_score":0.4548178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133265870976567,"score_gpt":0.2238124927370183,"score_spread":0.2124798340272526,"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."}}