{"id":"W2287466464","doi":"","title":"Saving Electrical Energy in Commercial Buildings","year":2012,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Architectural engineering; Electric potential energy; Energy (signal processing); Environmental science; Engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003248373,0.0007380384,0.0004259597,0.0007299289,0.0004294352,0.00185124,0.0007034446,0.0006814503,0.005460942],"category_scores_gemma":[0.001341764,0.0003556684,0.0003501492,0.001174778,0.0002481759,0.001501097,0.0007013808,0.0004530971,0.001768034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036098,"about_ca_system_score_gemma":0.0005785872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006039052,"about_ca_topic_score_gemma":0.006515519,"domain_scores_codex":[0.9997173,0.00004913143,0.00001451335,0.00007570849,0.0001139394,0.00002936983],"domain_scores_gemma":[0.9997978,0.00007629087,0.00002673499,0.00003716652,0.00005037439,0.0000117415],"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":[0.0001527798,0.0001291216,0.01067013,0.0002146236,0.00003272439,0.0001303858,0.0002336294,0.6061328,0.00878109,0.01252055,0.007436884,0.3535652],"study_design_scores_gemma":[0.00001426279,0.00006750773,0.00383375,0.00005314046,0.0000136627,0.00009116087,0.0001918947,0.9523515,0.01190812,0.01273439,0.01871769,0.00002296577],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3516732,0.003004264,0.5545131,0.001621556,0.0002000082,0.000417395,0.002442958,0.008587832,0.07753965],"genre_scores_gemma":[0.8362684,0.001512315,0.1512778,0.0001165753,0.00003305009,0.0001333736,0.001369654,0.000312675,0.008976034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006039052,"threshold_uncertainty_score":0.0182687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005672163543657619,"score_gpt":0.1725686613869993,"score_spread":0.1668964978433417,"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."}}