{"id":"W2082566529","doi":"10.1016/j.enbuild.2014.05.014","title":"Using suite energy-use and interior condition data to improve energy modeling of a 1960s MURB","year":2014,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Suite; Energy modeling; Roof; Energy (signal processing); Work (physics); Computer science; Electricity; Engineering; Efficient energy use; Simulation; Civil engineering; Mechanical engineering","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":[0.0003188301,0.0004870672,0.0006105233,0.0007501667,0.0006657204,0.0007962176,0.0006542151,0.0007111378,0.002703103],"category_scores_gemma":[0.001264974,0.0005512688,0.0008715703,0.0008738842,0.0002359551,0.000764977,0.0004191599,0.0009050686,0.0007086654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001369229,"about_ca_system_score_gemma":0.0008306013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1216844,"about_ca_topic_score_gemma":0.1330019,"domain_scores_codex":[0.9998647,0.00003658163,0.000008527686,0.00003188885,0.00004016293,0.00001811984],"domain_scores_gemma":[0.9997382,0.00009093295,0.00001673658,0.0000513515,0.00008842249,0.00001448518],"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":[0.00004522399,0.0000655885,0.0099511,0.00001695672,0.00001916855,0.00005338327,0.00004527322,0.9773862,0.001315993,0.001063316,0.0008308731,0.00920691],"study_design_scores_gemma":[0.000003789145,0.000007746593,0.002844927,0.000002760463,0.000004425348,0.000006433774,0.00001547261,0.9953187,0.000770627,0.0003001715,0.0007180044,0.000006956064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8985212,0.0001286775,0.07510994,0.0002535,0.00006996047,0.00004564948,0.004124526,0.0009111796,0.02083544],"genre_scores_gemma":[0.9804592,0.00005044054,0.01367179,0.00002534965,0.000009230304,0.00003290899,0.002321212,0.0002622973,0.003167542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1216844,"threshold_uncertainty_score":0.2419522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02493516910396057,"score_gpt":0.2299351440272715,"score_spread":0.2049999749233109,"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."}}