{"id":"W4410186776","doi":"10.1177/17442591251333433","title":"Enhancing building energy performance prediction: A fusion of deep learning and first-principles simulation methods","year":2025,"lang":"en","type":"article","venue":"Journal of Building Physics","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Office of Energy Research and Development","keywords":"Fusion; Deep learning; Energy (signal processing); Computer science; Architectural engineering; Artificial intelligence; Environmental science; Engineering; Mathematics","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":[0.0003852164,0.0007162621,0.0005677192,0.0004569808,0.0001993469,0.0006744771,0.0008088189,0.0006274924,0.0009543574],"category_scores_gemma":[0.001119337,0.0004167412,0.0005863919,0.0004177729,0.0002637398,0.0009805346,0.0007053051,0.0009154881,0.0002803462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005756239,"about_ca_system_score_gemma":0.0007764742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008069492,"about_ca_topic_score_gemma":0.009201129,"domain_scores_codex":[0.9998646,0.00002798337,0.000007324542,0.00002437171,0.00005749601,0.00001832087],"domain_scores_gemma":[0.9997341,0.0001206239,0.00003179447,0.00002976932,0.00006519038,0.00001842892],"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":[0.00001961931,0.00004742137,0.0009510596,0.00003635368,0.00003009466,0.00002313757,0.00001690422,0.9705842,0.001911928,0.001023691,0.0003412962,0.02501428],"study_design_scores_gemma":[8.76288e-7,0.00000400684,0.00007801951,0.000001979588,0.00000176258,0.000001961805,0.000001061512,0.9991648,0.0002525473,0.0004028921,0.00008844538,0.000001555193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1354175,0.0009349291,0.8517447,0.0006751699,0.0001402672,0.00005700515,0.0003150915,0.002199363,0.00851606],"genre_scores_gemma":[0.9551156,0.0004499187,0.04242552,0.0001016414,0.0000436039,0.00005363008,0.0002759946,0.00009196246,0.001442186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008069492,"threshold_uncertainty_score":0.01604503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009508701835179881,"score_gpt":0.2634423751237938,"score_spread":0.2539336732886139,"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."}}