{"id":"W4210640095","doi":"10.1080/19401493.2021.2019309","title":"Regulating window operations using HVAC terminal devices’ control sequences: a simulation-based investigation","year":2022,"lang":"en","type":"article","venue":"Journal of Building Performance Simulation","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Carleton University","funders":"National Research Council Canada","keywords":"Thermostat; HVAC; Window (computing); Closing (real estate); Efficient energy use; Terminal (telecommunication); Control (management); Indoor air quality; Energy (signal processing); Engineering; Simulation; Computer science; Automotive engineering; Real-time computing; Air conditioning; Operating system; Mechanical engineering; Telecommunications; Electrical engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006475663,0.0001749213,0.0002285822,0.0004150873,0.0006559044,0.00008988223,0.0001806532,0.00006878773,0.00004458309],"category_scores_gemma":[0.00006732254,0.0001880778,0.00009143101,0.0005497404,0.0000298395,0.001067846,0.00001920002,0.0003428119,2.67837e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004252024,"about_ca_system_score_gemma":0.0001527784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008172194,"about_ca_topic_score_gemma":0.000002510277,"domain_scores_codex":[0.9983616,0.00009137869,0.0007134487,0.0001315846,0.0004916959,0.0002102166],"domain_scores_gemma":[0.9990043,0.0002183017,0.0003248315,0.0001421749,0.000238016,0.00007235132],"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":[0.00004444433,0.00001372419,0.009521568,0.00004052656,0.00002947057,0.000002201318,0.0001744437,0.9826875,0.006032693,0.00003256197,0.000002229928,0.001418659],"study_design_scores_gemma":[0.0008827067,0.0001128104,0.001629828,0.00011445,0.00006384154,0.00002215926,0.00004752653,0.9951116,0.001669322,0.00005276901,0.00008900531,0.0002039378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6577578,0.00005834382,0.3416493,0.00002358562,0.0003343177,0.0001021876,0.000003349411,0.00005926419,0.00001189506],"genre_scores_gemma":[0.982686,0.000002658648,0.01687378,0.0001094125,0.0002592932,0.000008623819,0.00001618006,0.00003588573,0.000008123196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3249283,"threshold_uncertainty_score":0.7669592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026614996460285,"score_gpt":0.2546053238064256,"score_spread":0.2343391738418227,"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."}}