{"id":"W2015301487","doi":"10.1080/00038628.2000.9697436","title":"Selection Of Energy Conservation Measures in a Large Office Building using Decision Models under Uncertainty","year":2000,"lang":"en","type":"article","venue":"Architectural Science Review","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Office of Electricity","keywords":"Payback period; Energy conservation; Energy consumption; Selection (genetic algorithm); Energy (signal processing); Computer science; Reliability engineering; Efficient energy use; Operations research; Environmental economics; Engineering; Production (economics); Statistics; Economics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002027723,0.0008214301,0.001179459,0.001358121,0.0004515684,0.001469656,0.0006592519,0.0006084159,0.001027809],"category_scores_gemma":[0.003880684,0.0002940116,0.0007625075,0.001028114,0.0003383528,0.0007661479,0.0004421023,0.0003607341,0.0001034711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002012864,"about_ca_system_score_gemma":0.001540096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01800452,"about_ca_topic_score_gemma":0.01996903,"domain_scores_codex":[0.999116,0.0004591775,0.00003380543,0.00006602027,0.0002026283,0.0001223849],"domain_scores_gemma":[0.9983144,0.001363373,0.0001027452,0.00005445524,0.000121004,0.00004403322],"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":[0.0001731028,0.00007519721,0.003323892,0.00005869852,0.00003462866,0.0001177586,0.00003170213,0.9685858,0.001537975,0.001688754,0.0001840238,0.02418852],"study_design_scores_gemma":[0.00002609952,0.00008653542,0.001951186,0.00001245485,0.00003461247,0.00001590144,0.00008380585,0.9941568,0.00204297,0.001311341,0.0002617686,0.00001640738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8794137,0.0004301026,0.1137962,0.0002429625,0.00001052562,0.0002009235,0.0002572901,0.0002516883,0.00539671],"genre_scores_gemma":[0.9824429,0.0001924526,0.01680355,0.00001144476,0.000003836948,0.0000544604,0.0001154211,0.00001068585,0.0003652836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01800452,"threshold_uncertainty_score":0.03579944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0236631585763264,"score_gpt":0.2660794839005695,"score_spread":0.2424163253242431,"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."}}