{"id":"W4256700918","doi":"10.1109/seams.2015.20","title":"Adaptive Management of Energy Consumption Using Adaptive Runtime Models","year":2015,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria","keywords":"Computer science; Energy consumption; Feature extraction; Scheduling (production processes); Classifier (UML); Data mining; Schedule; Data center; Energy management; Artificial intelligence; Real-time computing; Machine learning; Energy (signal processing); Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002884689,0.0001361413,0.0001683677,0.0001242708,0.00005867933,0.00003907155,0.0005384948,0.00003563355,0.000006014423],"category_scores_gemma":[0.000001137748,0.000119314,0.00006315958,0.0002594691,0.0000489214,0.00006414596,0.0007990684,0.00004688497,0.00001520745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008720731,"about_ca_system_score_gemma":0.00002306026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001992085,"about_ca_topic_score_gemma":0.000002761638,"domain_scores_codex":[0.9987312,0.00006701083,0.0002368455,0.0003423191,0.0003959596,0.0002266788],"domain_scores_gemma":[0.9992048,0.00002248759,0.0001247927,0.0004360526,0.0001114797,0.0001003827],"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":[0.00001628959,0.00007732563,0.00001770422,0.00001047021,0.0001025639,0.00001707083,0.0003455297,0.4102641,0.000009188557,0.5677196,0.0003974313,0.02102276],"study_design_scores_gemma":[0.0004012973,0.00009659678,0.00006096806,0.00005725567,0.00002064093,0.000004749678,0.0002894528,0.9864313,0.0002101888,0.0120609,0.0002178908,0.0001487837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02607477,0.0001629053,0.9280986,0.00005620207,0.0001681601,0.0001004802,4.941951e-7,0.0001331489,0.04520522],"genre_scores_gemma":[0.8231577,0.000005081488,0.175539,0.00007777804,0.00002298308,0.000003465115,3.805722e-7,0.00000662797,0.001187012],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7970829,"threshold_uncertainty_score":0.4865485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09146527509437247,"score_gpt":0.2586015785255639,"score_spread":0.1671363034311914,"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."}}