{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003821418,0.0003792378,0.0003325383,0.0004977795,0.0002486681,0.0008392795,0.0004162561,0.0002453208,0.00183204],"category_scores_gemma":[0.0008405064,0.0001572355,0.0002739235,0.0004898046,0.0002200461,0.0007712583,0.0003141698,0.0002586973,0.0002149462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005963814,"about_ca_system_score_gemma":0.0004557223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201528,"about_ca_topic_score_gemma":0.003184018,"domain_scores_codex":[0.9997863,0.00003304604,0.000009126577,0.00003569886,0.0000883326,0.00004742659],"domain_scores_gemma":[0.9997693,0.00007865363,0.00002779628,0.00001877438,0.00008654765,0.00001895856],"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":[0.0006175536,0.0003306945,0.0281109,0.0001214826,0.00009349264,0.0000984256,0.00008113305,0.7746882,0.08101752,0.009468717,0.001050895,0.1043211],"study_design_scores_gemma":[0.00000723226,0.0001282883,0.02474545,0.000007354094,0.00002286617,0.00004053896,0.00005534418,0.9523902,0.01942628,0.002215967,0.0009457353,0.00001476041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7714266,0.0005926247,0.2098669,0.0003028943,0.00006802713,0.0001334775,0.0003186213,0.0003534668,0.01693734],"genre_scores_gemma":[0.9943868,0.00005629972,0.003498436,0.000008548649,0.000007121795,0.00001525779,0.00006678994,0.00002088785,0.001939967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00201528,"threshold_uncertainty_score":0.006128788,"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."}}