{"id":"W4386432516","doi":"10.1007/978-981-19-9822-5_99","title":"Evaluating and Mapping Indoor Thermal Risk on Older People in Long-Term Care Buildings Under Urban Microclimate Impacts","year":2023,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; National Research Council Canada","funders":"","keywords":"Microclimate; Environmental science; Meteorology; Weather Research and Forecasting Model; Thermal comfort; Limiting; Urban heat island; Extreme heat; Architectural engineering; Term (time); Climatology; Geography; Climate change; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000199059,0.000404979,0.0002761274,0.0002879633,0.0001660535,0.0008468017,0.0002716406,0.0003120188,0.002112236],"category_scores_gemma":[0.0005804769,0.0001441747,0.0003899699,0.0004829821,0.0001117284,0.0004966661,0.0003710814,0.0002039945,0.0003492379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003791465,"about_ca_system_score_gemma":0.0002495669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01278272,"about_ca_topic_score_gemma":0.03820028,"domain_scores_codex":[0.9999099,0.0000212474,0.000004209757,0.00001449082,0.00003511325,0.00001509828],"domain_scores_gemma":[0.9998938,0.00005299679,0.0000162974,0.000003985661,0.00002278568,0.00001006708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003191422,0.0002208764,0.4458835,0.0004509115,0.0003141733,0.0006371939,0.001459457,0.172587,0.01033308,0.003404901,0.006939337,0.3574505],"study_design_scores_gemma":[0.000008981491,0.000559014,0.7606515,0.0002151754,0.0002354931,0.0005273945,0.006206193,0.2071389,0.005403884,0.01025058,0.00873604,0.000066851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949679,0.001805326,0.02258037,0.0002816624,0.0000405649,0.00005136993,0.001181751,0.00007401998,0.02430612],"genre_scores_gemma":[0.9774392,0.001856024,0.009903845,0.00004302063,0.0000204819,0.00002739507,0.0007444788,0.00002247672,0.009943073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01278272,"threshold_uncertainty_score":0.02541661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00835883390148964,"score_gpt":0.2037122922324184,"score_spread":0.1953534583309288,"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."}}