{"id":"W4396229344","doi":"10.1080/19401493.2024.2346833","title":"Retraining surrogate models in increasingly restricted design spaces: a novel building energy model calibration method","year":2024,"lang":"en","type":"article","venue":"Journal of Building Performance Simulation","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Surrogate model; Latin hypercube sampling; Calibration; Energy (signal processing); Building energy simulation; Computer science; Mathematical optimization; Algorithm; Polynomial; Mathematics; Statistics; Energy performance; Monte Carlo method","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002302485,0.001208789,0.001276394,0.0006278002,0.0002741307,0.0006680787,0.00154633,0.001419122,0.002585371],"category_scores_gemma":[0.007047777,0.0007576813,0.001024551,0.00058635,0.000533569,0.001109443,0.001431599,0.001999472,0.0008254864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003995152,"about_ca_system_score_gemma":0.0008669804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001592,"about_ca_topic_score_gemma":0.001286194,"domain_scores_codex":[0.999071,0.0004559202,0.00004295196,0.0001299847,0.000238853,0.00006114651],"domain_scores_gemma":[0.9979989,0.001085519,0.0002185573,0.0003054618,0.0003247831,0.00006675257],"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":[0.00004190494,0.0000488439,0.0004212554,0.00004243134,0.00003390486,0.00003838964,0.00003723095,0.9543632,0.001763654,0.002444071,0.0005883404,0.04017672],"study_design_scores_gemma":[0.000003867802,0.00002104432,0.00004018813,0.000004500971,0.000002934813,0.000008571214,0.000002298553,0.9986619,0.0002860623,0.0007038722,0.0002621765,0.000002690374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01057942,0.0001414099,0.9872528,0.00009085235,0.0000203519,0.00003119811,0.00002849069,0.0005740934,0.001281377],"genre_scores_gemma":[0.5105114,0.0002084316,0.4844782,0.00031997,0.00006214987,0.0003582572,0.0003331817,0.000418141,0.00331036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002585371,"threshold_uncertainty_score":0.01217681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04002775762091131,"score_gpt":0.2757304122420546,"score_spread":0.2357026546211433,"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."}}