{"id":"W4391424960","doi":"10.1007/978-3-031-35471-7_46","title":"Implementing Surrogate Modeling Techniques for Designing Optimal Building Envelops: A Case Study","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Surrogate model; Computer science; Machine learning","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":[0.001945223,0.000655062,0.0006372328,0.0004576127,0.0005412166,0.001586633,0.0008045741,0.001610374,0.002669974],"category_scores_gemma":[0.004417273,0.0004287229,0.0006619418,0.001148999,0.0004591733,0.0009629008,0.001000368,0.0009817021,0.0004297725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004778193,"about_ca_system_score_gemma":0.000645234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001214398,"about_ca_topic_score_gemma":0.00208957,"domain_scores_codex":[0.9989704,0.0006237321,0.00003167183,0.00004680749,0.0002538638,0.0000735519],"domain_scores_gemma":[0.997595,0.001779537,0.00007984846,0.0002714302,0.0002194791,0.00005463622],"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":[0.0002649075,0.0002788668,0.001077667,0.0001312411,0.00002128315,0.0005049832,0.0003440624,0.8878456,0.007491765,0.01489117,0.001057517,0.08609086],"study_design_scores_gemma":[0.00002171672,0.0001430684,0.0002472715,0.0000160745,0.00001007808,0.0001306732,0.0001275755,0.9882219,0.005601477,0.003392372,0.002075331,0.00001246889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.207392,0.0002633723,0.7748905,0.0003617518,0.00003410501,0.0001415675,0.0001347183,0.0004591128,0.01632296],"genre_scores_gemma":[0.7412238,0.0001759373,0.2540855,0.00002975955,0.000006352582,0.00008319935,0.00009064018,0.0001400116,0.004164785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002669974,"threshold_uncertainty_score":0.01028746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160083809697802,"score_gpt":0.2421860859824289,"score_spread":0.2261777050126487,"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."}}