{"id":"W4308520947","doi":"10.1080/19401493.2022.2137236","title":"Improved calibration of building models using approximate Bayesian calibration and neural networks","year":2022,"lang":"en","type":"article","venue":"Journal of Building Performance Simulation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Resources Canada","keywords":"Calibration; Bayesian inference; Inference; Sensitivity (control systems); Computer science; Artificial neural network; Bayesian probability; Approximate Bayesian computation; Uncertainty quantification; Frequentist inference; Computation; Monte Carlo method; Variable-order Bayesian network; Bayesian network; Machine learning; Algorithm; Artificial intelligence; Statistics; Mathematics; 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":[0.001973757,0.0007150917,0.000652002,0.0007070767,0.0004387144,0.001075928,0.001138846,0.0009419951,0.00212278],"category_scores_gemma":[0.009484109,0.0006934997,0.0006251205,0.0007685434,0.0006536582,0.00159911,0.001187747,0.001681488,0.0004125355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001394574,"about_ca_system_score_gemma":0.001313562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01493448,"about_ca_topic_score_gemma":0.01580452,"domain_scores_codex":[0.999161,0.0004021479,0.00004078069,0.0001226958,0.0002268559,0.00004646631],"domain_scores_gemma":[0.9975069,0.001469591,0.0002830997,0.0002983292,0.0004012169,0.00004086835],"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":[0.000008047482,0.000007565644,0.0002874477,0.000007823616,0.000007806325,0.000005109933,0.000009516994,0.9898665,0.0002041266,0.002397636,0.00008742164,0.007111005],"study_design_scores_gemma":[0.000001168093,0.000001989306,0.00007584859,0.000002646272,9.677193e-7,0.000002115886,0.000001553417,0.9977968,0.0001009232,0.001935278,0.00007837055,0.000002303568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01874503,0.00007206091,0.978748,0.0001407868,0.0000142,0.00002302825,0.00005732685,0.0002908556,0.001908838],"genre_scores_gemma":[0.7944831,0.00018016,0.2026683,0.0001154728,0.00002746922,0.0001274667,0.000296765,0.0001595403,0.001941713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01493448,"threshold_uncertainty_score":0.02969509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06709170752371582,"score_gpt":0.315957788613406,"score_spread":0.2488660810896901,"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."}}