{"id":"W4396878448","doi":"10.2316/j.2024.201-0468","title":"LOW ENERGY OFFICE BUILDING DESIGN BASED ON NON-DOMINATED SORTING GENETIC ALGORITHM 2 AND EXTREME GRADIENT BOOSTING-ARTIFICIAL NEURAL NETWORK, 194-202. SI","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Sorting; Artificial neural network; Boosting (machine learning); Genetic algorithm; Computer science; Algorithm; Artificial intelligence; Gradient boosting; Sorting algorithm; Sorting network; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0003794595,0.0002918751,0.0003308371,0.0001323476,0.0001858724,0.0002598922,0.00008940833,0.000140226,0.00000534028],"category_scores_gemma":[0.000009796041,0.0002762637,0.00006494545,0.0002079279,0.0000216304,0.00009723154,0.00001837404,0.0001791681,7.65396e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009091688,"about_ca_system_score_gemma":0.0000337526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001307483,"about_ca_topic_score_gemma":0.00001016372,"domain_scores_codex":[0.9984342,0.00009310275,0.0004114016,0.000387219,0.0001798981,0.0004942149],"domain_scores_gemma":[0.9994224,0.0002022405,0.00006437198,0.0001724167,0.0000296241,0.0001089623],"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":[0.0000304776,0.00001010062,0.000008728129,0.00009699972,0.00006422451,0.00002904026,0.00002181327,0.9262206,0.001365791,0.001655199,0.00006457269,0.07043245],"study_design_scores_gemma":[0.0005842148,0.0001347427,0.00002965094,0.000348859,0.00008171004,0.00002699619,0.00002029504,0.997763,0.0003335849,0.0001115743,0.000278696,0.0002866487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01629968,0.009753861,0.9719028,0.00002678046,0.001352699,0.0002722952,0.000005797186,0.0003342458,0.00005179913],"genre_scores_gemma":[0.9966797,0.00006852873,0.002381705,0.00005360266,0.0005638936,0.000121113,0.000009287213,0.00007004625,0.00005213964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.98038,"threshold_uncertainty_score":0.9999689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00886154230015885,"score_gpt":0.1827159610950634,"score_spread":0.1738544187949046,"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."}}