{"id":"W4392628922","doi":"10.26868/25222708.2023.1632","title":"Energy performance of commercial buildings in partial-to-no- occupancy: Lessons learned from the COVID-19 pandemic lockdown in Canadian government buildings","year":2023,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Occupancy; Electricity; Thermal comfort; Computer science; Architectural engineering; Energy management; Coronavirus disease 2019 (COVID-19); Work (physics); Pandemic; Environmental science; Environmental economics; Energy (signal processing); Automotive engineering; Engineering; Meteorology; Statistics; Electrical engineering; Economics; Geography; Mathematics; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005418827,0.0004106109,0.0003495907,0.0002706907,0.0005444757,0.0007744174,0.0008177278,0.0004194689,0.0008769008],"category_scores_gemma":[0.001426202,0.0001600468,0.0004209334,0.0006685925,0.0006406506,0.0004871919,0.0002793438,0.0004480595,0.0001332757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00500672,"about_ca_system_score_gemma":0.002124192,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8518358,"about_ca_topic_score_gemma":0.8786544,"domain_scores_codex":[0.9997109,0.00003740287,0.000009308127,0.0000466604,0.00007934606,0.000116422],"domain_scores_gemma":[0.9995796,0.000150235,0.00003023546,0.00004189428,0.0001580502,0.00003997015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008813164,0.0005641893,0.4026734,0.0001790032,0.0001796555,0.0007239589,0.0009770097,0.5318421,0.00795559,0.003091539,0.003243775,0.04768854],"study_design_scores_gemma":[0.00002499625,0.0001849078,0.4770553,0.00002157269,0.00005273248,0.00006817032,0.002196459,0.5134429,0.004257139,0.0005087221,0.002133539,0.00005344718],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997875,0.00007504691,0.0004523886,0.0001084706,0.000003465448,0.000005194737,0.0002746566,0.00001814643,0.001187623],"genre_scores_gemma":[0.9993886,0.00003472375,0.0001251756,0.000006499656,0.000001184197,0.000001209255,0.0002442644,0.000004430377,0.0001939356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1481642,"threshold_uncertainty_score":0.2980735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06627298043650917,"score_gpt":0.295315205413292,"score_spread":0.2290422249767829,"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."}}