{"id":"W3171245171","doi":"10.1016/j.apenergy.2021.117050","title":"Energy, exergy and computing efficiency based data center workload and cooling management","year":2021,"lang":"en","type":"article","venue":"Applied Energy","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Workload; Data center; Cooling load; Rack; Efficient energy use; Exergy; Computer science; Coefficient of performance; Metric (unit); Environmental science; Reliability engineering; Simulation; Process engineering; Engineering; Air conditioning; Mechanical engineering; Operations management; Operating system; Electrical engineering; Heat exchanger","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":[0.000262404,0.0002238278,0.0002216407,0.0001143553,0.0002992692,0.0003206005,0.0008765829,0.00005407935,0.000004224632],"category_scores_gemma":[0.000004003374,0.0002208163,0.00002776709,0.0004476008,0.0000640291,0.00002649476,0.003941084,0.00008511893,0.000002151226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000228175,"about_ca_system_score_gemma":0.00002035292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006630374,"about_ca_topic_score_gemma":0.00001912747,"domain_scores_codex":[0.9979354,0.00005328622,0.0002701922,0.001015926,0.0002931759,0.0004320554],"domain_scores_gemma":[0.9984692,0.0001076456,0.00008845372,0.00117306,0.00002725579,0.0001343654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009046957,0.0001694941,0.00006748526,0.00005500181,0.00006611754,0.0001347834,0.0001178821,0.01821096,0.0001042185,0.2187902,0.0008902683,0.7613845],"study_design_scores_gemma":[0.0008864076,0.00001750845,0.0002707199,0.0001051502,0.00002026884,0.00002106607,0.00010106,0.9178537,0.0005162908,0.0007320342,0.07913238,0.0003434054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01691362,0.001333148,0.9597295,0.0004986599,0.0002600052,0.00005174536,0.000001824659,0.0002439108,0.02096763],"genre_scores_gemma":[0.9676147,0.0001116594,0.03001685,0.001726151,0.0001106788,0.000005362436,0.0000217412,0.0000175002,0.000375395],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9507011,"threshold_uncertainty_score":0.9004628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01315246959320512,"score_gpt":0.2131870052063266,"score_spread":0.2000345356131214,"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."}}