{"id":"W4245750689","doi":"10.1145/2637364.2592025","title":"Optimal energy source selection and capacity planning for green datacenters","year":2014,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Capital cost; Workload; Reliability engineering; Efficient energy use; Energy storage; Service (business); Energy supply; Energy (signal processing); Work (physics); Capital expenditure; Risk analysis (engineering); Power (physics); Engineering; Operating system; Business; Electrical engineering","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":[0.001132485,0.001196238,0.001087145,0.001078583,0.0006059192,0.001194929,0.001277897,0.0007585753,0.002133496],"category_scores_gemma":[0.001733687,0.000810041,0.0005373537,0.001238341,0.0007848518,0.001751881,0.0009205364,0.0009656675,0.000167968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002101999,"about_ca_system_score_gemma":0.002280814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008823469,"about_ca_topic_score_gemma":0.01393836,"domain_scores_codex":[0.9994087,0.0001790062,0.00002039601,0.0001186589,0.0001204136,0.0001528371],"domain_scores_gemma":[0.9993068,0.0003559315,0.00008309111,0.00004647721,0.0001150771,0.00009260108],"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":[0.00005542397,0.00004718966,0.0004527793,0.00006176118,0.00002279505,0.00003195742,0.0000288725,0.9663597,0.001369319,0.007772314,0.001285361,0.02251256],"study_design_scores_gemma":[0.0000170972,0.00003362681,0.0003386934,0.00001330256,0.0000131473,0.0000154694,0.00004633411,0.9860377,0.0008871532,0.01166475,0.0009232696,0.000009514602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1016114,0.002518061,0.8841498,0.001027275,0.00008179044,0.0002329604,0.0005352203,0.0007374078,0.009106115],"genre_scores_gemma":[0.8327988,0.0009148375,0.1638257,0.000103983,0.0000392308,0.0001079748,0.0003146737,0.0001366542,0.001758181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008823469,"threshold_uncertainty_score":0.01754421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06592006992444671,"score_gpt":0.3039295027620189,"score_spread":0.2380094328375721,"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."}}