{"id":"W3038878150","doi":"10.1016/j.enbuild.2020.110270","title":"Improved long-term thermal comfort indices for continuous monitoring","year":2020,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; University of British Columbia","funders":"China Scholarship Council; National Research Foundation Singapore","keywords":"Thermal comfort; Term (time); Environmental science; Statistics; Automotive engineering; Reliability engineering; Engineering; Computer science; Mathematics; Meteorology; Physics","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":[0.0005543843,0.0006290571,0.0005197414,0.0005725957,0.0001970865,0.0006489974,0.0004259428,0.0003566885,0.001048118],"category_scores_gemma":[0.0009820365,0.0001445292,0.0003281641,0.0007771675,0.0001120095,0.0007773919,0.0002980596,0.0005489084,0.000334083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002802045,"about_ca_system_score_gemma":0.0002065556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001315535,"about_ca_topic_score_gemma":0.002574523,"domain_scores_codex":[0.9997464,0.0000587186,0.00001226712,0.00006434766,0.00009392417,0.0000243371],"domain_scores_gemma":[0.9994488,0.000142261,0.00008946192,0.00008848158,0.000204329,0.00002663706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001789315,0.0007435819,0.0539865,0.0003164892,0.0002513563,0.0001166327,0.0001667927,0.1687573,0.2402611,0.001905866,0.004055865,0.5276491],"study_design_scores_gemma":[0.00001791948,0.0004034597,0.06151654,0.00002191972,0.0001317596,0.0001080825,0.0000531077,0.8753644,0.05958173,0.0006431958,0.002107717,0.00005018875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5144346,0.001411134,0.4766316,0.0001034681,0.000135694,0.00004391605,0.0009208664,0.001313214,0.005005518],"genre_scores_gemma":[0.9427373,0.0001434609,0.05499517,0.00002326554,0.00004304392,0.00003338679,0.0006620269,0.00007966248,0.001282727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001315535,"threshold_uncertainty_score":0.003506303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009247252376080511,"score_gpt":0.2014161166079714,"score_spread":0.1921688642318909,"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."}}