{"id":"W4253540691","doi":"10.32920/ryerson.14645958.v1","title":"Evaluating Performance of Southern Ontario Buildings Using Submetering data and Whole Building Modeling Results","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sustainable Building Design and Assessment","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Occupancy; Energy consumption; Energy performance; Post-occupancy evaluation; Computer science; Architectural engineering; Value (mathematics); Process (computing); Energy (signal processing); Building design; Order (exchange); Civil engineering; Engineering; Business; Statistics; Mathematics; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00172034,0.0004999642,0.0006852626,0.0002655864,0.0001400257,0.0003351696,0.0007281705,0.0002786823,0.00002246161],"category_scores_gemma":[0.0001114795,0.0005483946,0.00008453616,0.0001794091,0.00003590658,0.0003592824,0.002671412,0.0008546555,6.527701e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005612455,"about_ca_system_score_gemma":0.0004678977,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009273509,"about_ca_topic_score_gemma":0.0006900759,"domain_scores_codex":[0.9969808,0.00005705218,0.0009553162,0.0009504067,0.0004951839,0.0005612018],"domain_scores_gemma":[0.9980211,0.00008376922,0.0002125477,0.001364385,0.0002011703,0.0001170702],"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":[0.00002439946,0.00001318603,0.0008016037,0.001219752,0.0001436152,0.00000906475,0.001828566,0.9444545,0.04521304,0.000004307219,0.000007101616,0.006280864],"study_design_scores_gemma":[0.0004531774,0.00003033649,0.0000330592,0.00171307,0.0001561205,0.00001624954,0.0019817,0.9913003,0.003661022,0.0000529067,0.00004366722,0.000558354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8684839,0.0008173511,0.1295701,0.000008012812,0.0003599776,0.0003123903,0.00007482197,0.0002019018,0.0001716047],"genre_scores_gemma":[0.7694797,0.00007228245,0.230027,0.000005729622,0.00009456535,0.00001065829,0.0001132975,0.00008591729,0.000110867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1004569,"threshold_uncertainty_score":0.9996967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1184982123288881,"score_gpt":0.3271255159782478,"score_spread":0.2086273036493598,"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."}}