{"id":"W4312105463","doi":"10.1016/j.buildenv.2022.109914","title":"Quantifying improvement of building and zone level thermal resilience by cooling retrofits against summertime heat events","year":2022,"lang":"en","type":"article","venue":"Building and Environment","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":66,"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","keywords":"Resilience (materials science); Robustness (evolution); Index (typography); Environmental science; Extreme heat; Computer science; Architectural engineering; Extreme weather; Reliability engineering; Environmental resource management; Engineering; Climate change; Materials science","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":[0.000789802,0.000509998,0.0003364962,0.0005970818,0.0002657492,0.0006905681,0.0004488208,0.0006381195,0.001254349],"category_scores_gemma":[0.001792855,0.0002253091,0.0006072608,0.0006457,0.0003521191,0.0008426617,0.000326564,0.0003662322,0.0002135584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005356141,"about_ca_system_score_gemma":0.0003745952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007093788,"about_ca_topic_score_gemma":0.009920084,"domain_scores_codex":[0.9996483,0.00008963813,0.00002055718,0.00009591982,0.0000673087,0.00007813517],"domain_scores_gemma":[0.9990749,0.0003333131,0.0002045789,0.0001771128,0.0001431084,0.00006681569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001347007,0.0003579461,0.3375721,0.0001194581,0.0004789779,0.0001885999,0.0001860607,0.5965519,0.03437518,0.0009286363,0.0004057,0.02748832],"study_design_scores_gemma":[0.00003683861,0.001677555,0.6493581,0.00002284514,0.0003060021,0.0001145838,0.0006410177,0.3215385,0.02474542,0.0006757566,0.0008342721,0.00004906817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978801,0.00001597017,0.001197493,0.00001180404,0.00000332314,0.000008864208,0.0003016623,0.00002974343,0.000550986],"genre_scores_gemma":[0.9994397,0.000005818523,0.000324522,0.000001568756,9.494701e-7,0.000004612427,0.0001341906,0.00000333561,0.00008530798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007093788,"threshold_uncertainty_score":0.01410502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115985271115687,"score_gpt":0.2305126550783054,"score_spread":0.2093528023671485,"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."}}