{"id":"W7132939317","doi":"","title":"Lessons Learned from Building Energy Modelling and Hybrid Calibration: Institutional Building Case Study","year":2025,"lang":"","type":"dissertation","venue":"TSpace","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hudbay Minerals (Canada)","funders":"","keywords":"Calibration; Workflow; Residual; Energy (signal processing); Building energy simulation; Bridge (graph theory); Fidelity; Bayesian probability; Work (physics)","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":[0.005036412,0.0006944021,0.0005812603,0.0004368532,0.000816853,0.002926996,0.001732166,0.001306922,0.002383638],"category_scores_gemma":[0.008945358,0.0003871029,0.0005110633,0.001000088,0.001547922,0.001914243,0.001703154,0.001577225,0.0003665244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002369583,"about_ca_system_score_gemma":0.001753003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02416204,"about_ca_topic_score_gemma":0.04327312,"domain_scores_codex":[0.9978066,0.001330839,0.0000735685,0.0002106996,0.0004599372,0.0001183285],"domain_scores_gemma":[0.9961272,0.002576836,0.00009347953,0.0006599957,0.0004454856,0.00009706456],"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":[0.0001090397,0.0001256837,0.00880585,0.0002742609,0.00006190644,0.000351436,0.002314547,0.8201476,0.001716202,0.05820209,0.003130365,0.1047609],"study_design_scores_gemma":[0.00005821674,0.0001123746,0.003872672,0.0002297185,0.00004270824,0.0002128024,0.002659299,0.9041759,0.01073428,0.03853731,0.03926912,0.00009562795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3760124,0.001213894,0.5742131,0.004478997,0.00009236451,0.0001789025,0.0005946243,0.001698306,0.04151748],"genre_scores_gemma":[0.855457,0.0003714639,0.1413709,0.0001013372,0.00001910964,0.00004959615,0.0002368511,0.0002971611,0.002096582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02416204,"threshold_uncertainty_score":0.04804283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04950011053957117,"score_gpt":0.3141836889629754,"score_spread":0.2646835784234043,"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."}}