{"id":"W2055732821","doi":"10.1109/iccchina.2012.6356938","title":"Cost efficient datacenter selection for cloud services","year":2012,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cloud computing; Bandwidth (computing); Distributed computing; Electricity; Bundle; Computer network; Subgradient method; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003361996,0.00008391939,0.00007015814,0.00005010085,0.0001538809,0.0001135322,0.0004431905,0.00002363183,0.00001030015],"category_scores_gemma":[0.00000329265,0.00006515579,0.00004361719,0.0001740439,0.00000714897,0.00003090288,0.0003224053,0.00004065244,0.00007978167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003208695,"about_ca_system_score_gemma":0.000004588333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002737644,"about_ca_topic_score_gemma":0.000005797477,"domain_scores_codex":[0.9991871,0.00002193595,0.0001131095,0.0002036983,0.0001399467,0.0003342537],"domain_scores_gemma":[0.9995345,0.0000414084,0.00004154251,0.0002693603,0.00003323617,0.00007997209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006234669,0.002307909,0.04224283,0.0005860442,0.0002552184,0.000001442432,0.01003057,0.1209244,0.000689278,0.3967846,0.09909239,0.327023],"study_design_scores_gemma":[0.0002294263,0.00002673283,0.001134034,0.00001008523,0.000005516586,0.000003828299,0.00006416893,0.7790124,0.0004844423,0.00003697042,0.2188874,0.0001049917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.127539,0.00005571,0.8668455,0.0005935216,0.001164883,0.00031566,9.114622e-7,0.0002704192,0.003214428],"genre_scores_gemma":[0.972247,5.294959e-7,0.02463543,0.0008376812,0.0005135637,0.00002294322,0.000003402999,0.000006702062,0.001732769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.844708,"threshold_uncertainty_score":0.2656976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02327198008392745,"score_gpt":0.2641388551561042,"score_spread":0.2408668750721768,"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."}}