{"id":"W4244788906","doi":"10.1109/iccps.2016.7479104","title":"GreenPlanning: Optimal Energy Source Selection and Capacity Planning for Green Datacenters","year":2016,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Cloud computing; Workload; Capital cost; Reliability engineering; Data center; Efficient energy use; Service provider; Energy (signal processing); Capacity planning; Service (business); Distributed computing; Computer network; Operating system; Engineering; Business","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":[0.0009755967,0.001149662,0.0009568143,0.0007677685,0.0005928961,0.001019676,0.00113998,0.0007030094,0.002812669],"category_scores_gemma":[0.001786914,0.0005740769,0.0005411145,0.0008781198,0.0007612087,0.001233488,0.001079721,0.0008922202,0.00021766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001443038,"about_ca_system_score_gemma":0.002343497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008802468,"about_ca_topic_score_gemma":0.01308019,"domain_scores_codex":[0.9995827,0.0001224857,0.00001329397,0.00008637426,0.00008793156,0.0001071096],"domain_scores_gemma":[0.9994346,0.0002966416,0.00006795301,0.00004048947,0.0000764703,0.00008375408],"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":[0.00005305421,0.00003825771,0.0003550513,0.00003454082,0.00001323308,0.00003009376,0.00002827049,0.9751923,0.0009761538,0.005340295,0.0009911067,0.01694768],"study_design_scores_gemma":[0.00001149664,0.00002455897,0.0001223317,0.00000719465,0.00000656674,0.000009467801,0.00002227232,0.9927061,0.0005656554,0.005935751,0.0005818931,0.000006680764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06167247,0.0008250627,0.9284755,0.0004832925,0.00006855073,0.0001899902,0.0003275244,0.0009922029,0.006965402],"genre_scores_gemma":[0.8188926,0.000378582,0.1780234,0.0001024101,0.00003157413,0.0001428359,0.0002385804,0.000178116,0.002011934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008802468,"threshold_uncertainty_score":0.01750249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02550847405985944,"score_gpt":0.2328043225940762,"score_spread":0.2072958485342167,"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."}}