{"id":"W4399793772","doi":"10.32920/26052418.v1","title":"Chiller Performance Evaluation and Optimization Algorithms for Existing Buildings","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Sciencetech (Canada)","funders":"","keywords":"Chiller; Chiller boiler system; Computer science; Optimization algorithm; Architectural engineering; Algorithm; Water chiller; Mathematical optimization; Engineering; Mechanical engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001226947,0.000803276,0.0006388729,0.0005888526,0.0003678357,0.001069891,0.0007675052,0.0008063368,0.003205619],"category_scores_gemma":[0.002486937,0.0003107924,0.0005930145,0.0005483392,0.0003475432,0.0006231786,0.0005497278,0.0008742698,0.0005934872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001305247,"about_ca_system_score_gemma":0.0008875981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0094645,"about_ca_topic_score_gemma":0.007901402,"domain_scores_codex":[0.999588,0.0001385629,0.00001977877,0.00006548546,0.0001395259,0.00004863627],"domain_scores_gemma":[0.9992185,0.0004082302,0.00005856861,0.00006555134,0.0002255818,0.00002347135],"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":[0.00003637602,0.00004133887,0.000545875,0.00003496253,0.00001405134,0.000008437749,0.00001418309,0.9707501,0.0008046004,0.001233835,0.0003130536,0.02620329],"study_design_scores_gemma":[0.000003010873,0.00001264513,0.0001577794,0.000002333457,0.000001931477,0.000002011697,0.000002753492,0.9990156,0.0004301193,0.0002196624,0.0001508615,0.000001284565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1126559,0.0007905209,0.8700883,0.0002808009,0.00005216232,0.0001287197,0.0002377431,0.001937123,0.01382869],"genre_scores_gemma":[0.7736824,0.0003854996,0.2184474,0.00005609629,0.0000385571,0.0001781543,0.0004477061,0.0002727016,0.006491481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0094645,"threshold_uncertainty_score":0.01881886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03349548848688475,"score_gpt":0.2752431664840839,"score_spread":0.2417476779971992,"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."}}