{"id":"W4288844211","doi":"10.1016/j.energy.2022.124890","title":"Advanced smart trigeneration energy system design for commercial building applications – Energy and cost performance analyses","year":2022,"lang":"en","type":"article","venue":"Energy","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Renewable energy; Greenhouse gas; HVAC; Photovoltaic system; Engineering; Heat pump; Process engineering; Environmental economics; Systems engineering; Electrical engineering; Air conditioning; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001363394,0.0001937526,0.0002151949,0.0001867174,0.0006789189,0.0000455516,0.0001663833,0.00006816509,0.00001417849],"category_scores_gemma":[0.000003657923,0.000221604,0.00005710834,0.0003699645,0.00002059375,0.0001663604,0.00005217544,0.00005927204,8.514056e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001853767,"about_ca_system_score_gemma":0.00003001758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001241119,"about_ca_topic_score_gemma":0.00005674373,"domain_scores_codex":[0.9989769,0.00006551493,0.0002801482,0.0002633354,0.0001580974,0.0002560427],"domain_scores_gemma":[0.9994938,0.00009028421,0.00007430312,0.0002224364,0.00004768553,0.00007154117],"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":[0.00003198968,0.00001605408,0.000007271026,0.00002001125,0.00003962964,4.162393e-7,0.00001391067,0.8725982,0.003282863,0.06990918,0.0009574379,0.05312306],"study_design_scores_gemma":[0.0003777909,0.0000588379,0.00000984269,0.000008515469,0.00003224518,0.000009625524,0.00002973868,0.7864231,0.01885345,0.0001083713,0.1938682,0.0002202981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003088783,0.001078076,0.994387,0.00002357127,0.0003521152,0.0001135435,0.00002155942,0.0003400295,0.0005953026],"genre_scores_gemma":[0.9811329,0.000341676,0.01436714,0.0001186749,0.0002110101,0.003069661,0.0002838814,0.0000598499,0.0004152677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9800199,"threshold_uncertainty_score":0.9036748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465844391874803,"score_gpt":0.2445438532445325,"score_spread":0.2198854093257845,"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."}}