{"id":"W2114590246","doi":"10.1109/clustr.2007.4629271","title":"Improving System Efficiency through Scheduling and Power Management","year":2007,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Power consumption; Scheduling (production processes); Power management; System on a chip; Embedded system; Power (physics); Distributed computing; Computer architecture; Engineering; Operations management","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":[0.0005542177,0.00008581774,0.00008023155,0.00007763421,0.0001477777,0.000135014,0.0003295416,0.00003312974,0.000001579937],"category_scores_gemma":[0.00000660534,0.00007416955,0.00002039974,0.0002477402,0.00001790511,0.0002228664,0.0002948367,0.00005477771,0.000008382339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000315283,"about_ca_system_score_gemma":0.000006746615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001630906,"about_ca_topic_score_gemma":4.393341e-7,"domain_scores_codex":[0.999153,0.00001383276,0.0001801652,0.0002814099,0.0001482588,0.0002233104],"domain_scores_gemma":[0.9995623,0.00003208423,0.00005537412,0.0002689084,0.00003791898,0.0000434716],"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":[0.000002234776,0.00002961235,0.0001580063,0.00006311604,0.000009492775,0.00003019403,0.0004885608,0.002133582,0.0001263663,0.968735,0.000105804,0.02811806],"study_design_scores_gemma":[0.0002964057,0.00007299402,0.0005287901,0.0000769594,0.000004943492,0.00004397115,0.0003931793,0.9923503,0.004484357,0.000706528,0.0007433915,0.000298145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003719279,0.0001215092,0.9202315,0.00005334757,0.0001285543,0.000107483,3.991057e-8,0.0009799692,0.07465839],"genre_scores_gemma":[0.4975371,0.000003499044,0.5022214,0.0000941567,0.000007552797,9.867258e-7,8.3671e-8,0.000002616732,0.0001325611],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9902167,"threshold_uncertainty_score":0.3024546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009472723249255745,"score_gpt":0.2449560519684097,"score_spread":0.235483328719154,"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."}}