{"id":"W4386432428","doi":"10.1007/978-981-19-9822-5_151","title":"Quantifying Energy Usage and Savings in IoT Control Retrofitted Canadian Small Commercial Buildings","year":2023,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Thermostat; Upgrade; Electricity; Portfolio; Architectural engineering; Efficient energy use; Computer science; Environmental economics; Engineering; Environmental science; Business; Electrical engineering; Economics; Finance; Operating system","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002845185,0.0006476627,0.0002786182,0.0007123476,0.0008677999,0.001268253,0.0009324472,0.0002904122,0.002150781],"category_scores_gemma":[0.0006697541,0.0002301119,0.0005714848,0.002606596,0.000533449,0.0005519444,0.000243914,0.0003147355,0.0002163554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01816578,"about_ca_system_score_gemma":0.004826997,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9575546,"about_ca_topic_score_gemma":0.9820987,"domain_scores_codex":[0.9997078,0.00001739257,0.000006491571,0.00004077068,0.0001667388,0.00006088298],"domain_scores_gemma":[0.9997925,0.00004931888,0.00001353823,0.00001283566,0.0001224665,0.000009267724],"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":[0.0007752026,0.0002175463,0.07737241,0.0002815526,0.000187498,0.0003119458,0.0004660089,0.722442,0.01975435,0.01291224,0.00823859,0.1570407],"study_design_scores_gemma":[0.00002563513,0.0002724863,0.3338023,0.00008775551,0.0001924481,0.0001246362,0.002702594,0.6056913,0.03182132,0.002599822,0.02254991,0.0001297374],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642138,0.0007346682,0.006521503,0.0001317381,0.00002684417,0.00005710097,0.003393406,0.0001121344,0.02480873],"genre_scores_gemma":[0.9860502,0.0003845674,0.002653589,0.00001454299,0.000002351107,0.00001339354,0.001427382,0.00004290693,0.009411105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04244542,"threshold_uncertainty_score":0.1318026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103990996947618,"score_gpt":0.1608642209776975,"score_spread":0.1504651212829357,"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."}}