{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002986763,0.0004145002,0.0002977259,0.0004715669,0.0002391112,0.000425817,0.0003505626,0.0006663403,0.0007320878],"category_scores_gemma":[0.0006050558,0.0001777251,0.0003755061,0.0006044065,0.0002286353,0.0007641361,0.0001844339,0.0005006665,0.0003163832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003968654,"about_ca_system_score_gemma":0.0002708231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00729632,"about_ca_topic_score_gemma":0.01396128,"domain_scores_codex":[0.9998758,0.00001909057,0.000006247828,0.00004285811,0.00003348567,0.00002259765],"domain_scores_gemma":[0.9996743,0.0001388673,0.00003772099,0.00004695846,0.00007970462,0.00002237119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007169757,0.0009729561,0.2113644,0.0002248093,0.0002429642,0.000171133,0.0001552127,0.6674495,0.04597022,0.001045893,0.003463319,0.06822266],"study_design_scores_gemma":[0.00002462127,0.0001496782,0.1301365,0.00001393958,0.00003902896,0.00003173607,0.0001011622,0.8586165,0.009180672,0.0008445631,0.0008359962,0.00002556177],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843811,0.000135781,0.01120283,0.0000996087,0.00005079622,0.00002072223,0.001373995,0.0002346617,0.002500539],"genre_scores_gemma":[0.9969159,0.00003640574,0.001889326,0.00001310845,0.00001148568,0.00001053022,0.0007667584,0.000009512378,0.0003468398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00729632,"threshold_uncertainty_score":0.01450771,"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."}}