{"id":"W4319967909","doi":"10.1016/j.enbuild.2023.112851","title":"Digital twin with Machine learning for predictive monitoring of CO2 equivalent from existing buildings","year":2023,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":182,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Dashboard; Retrofitting; Computer science; Engineering; Systems engineering; Data science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004698755,0.0007198505,0.0006973276,0.001264569,0.0003042386,0.0007445219,0.0008300914,0.0006642672,0.001323615],"category_scores_gemma":[0.001320647,0.000268689,0.0003948981,0.001324878,0.0002738216,0.001053087,0.0008160174,0.0007539098,0.0005661959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003922403,"about_ca_system_score_gemma":0.000285195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004373881,"about_ca_topic_score_gemma":0.005789964,"domain_scores_codex":[0.999708,0.00004276678,0.00001205024,0.0001134368,0.00008397528,0.00003970333],"domain_scores_gemma":[0.999676,0.0001388838,0.00003383548,0.0000475947,0.00008385239,0.00001981395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001051034,0.0005877785,0.02561246,0.000219572,0.0001832787,0.0002160925,0.00009531923,0.290932,0.02925031,0.002054472,0.004139249,0.6456584],"study_design_scores_gemma":[0.000003826809,0.00002602781,0.001995825,0.000003068167,0.000008606479,0.00002047426,0.000009350294,0.9931639,0.0038957,0.0005898554,0.0002768738,0.00000649702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4266647,0.001170795,0.5605124,0.0002810347,0.000396279,0.00007682644,0.001157142,0.003029527,0.006711225],"genre_scores_gemma":[0.951228,0.000169067,0.04606977,0.00007055199,0.000065438,0.00003078299,0.0005083805,0.00004326343,0.00181475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004373881,"threshold_uncertainty_score":0.008696854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999692105007831,"score_gpt":0.2300050647487981,"score_spread":0.2100081436987198,"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."}}