{"id":"W4212923870","doi":"10.32920/19175855.v1","title":"The role of workload prediction in reducing datacentre energy costs","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workload; The Internet; Computer science; World Wide Web; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001757959,0.001215934,0.0006326362,0.0007963664,0.0006387234,0.002254131,0.001237169,0.0006231751,0.006129731],"category_scores_gemma":[0.0109635,0.0003301848,0.0003328974,0.0009232892,0.0003281701,0.002780702,0.0007878997,0.001120626,0.0017436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015358,"about_ca_system_score_gemma":0.001613163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0108011,"about_ca_topic_score_gemma":0.01354362,"domain_scores_codex":[0.9990472,0.0002899989,0.00003705577,0.000200732,0.0002814763,0.0001436199],"domain_scores_gemma":[0.995475,0.002342412,0.0003457974,0.0003280751,0.001089792,0.0004189579],"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":[0.0009492682,0.0007780332,0.04082777,0.0003029352,0.0001286572,0.0001315169,0.0001914374,0.2413054,0.005975605,0.008826324,0.06596097,0.6346221],"study_design_scores_gemma":[0.00004970987,0.0002197102,0.01296091,0.00009434608,0.0000387515,0.00006411823,0.0003141077,0.9629263,0.002635676,0.01075391,0.009901226,0.00004127323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4532642,0.01128983,0.3699243,0.03012374,0.002770691,0.0006359782,0.002544619,0.0078709,0.1215758],"genre_scores_gemma":[0.9674962,0.0008296436,0.02589012,0.0007302928,0.0002482131,0.00006218924,0.0006788349,0.0002221504,0.003842349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0108011,"threshold_uncertainty_score":0.02147645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007711443511593887,"score_gpt":0.2138034418664123,"score_spread":0.2060919983548184,"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."}}