{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005869846,0.0007687103,0.0006155713,0.0006288976,0.0004932198,0.001131995,0.001199795,0.0004130201,0.002857963],"category_scores_gemma":[0.002001649,0.0002473545,0.0002490012,0.001027316,0.0003841657,0.001519707,0.0006215003,0.0005420659,0.001019274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005408605,"about_ca_system_score_gemma":0.0009918545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009388403,"about_ca_topic_score_gemma":0.001732973,"domain_scores_codex":[0.9995122,0.00009468923,0.00003354571,0.00006754579,0.0002112009,0.00008071554],"domain_scores_gemma":[0.999267,0.0002557925,0.0001075898,0.0001524251,0.0001737586,0.00004342056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003204572,0.0004315896,0.002109531,0.0003799364,0.00008964459,0.0001228004,0.0002071084,0.2994684,0.1241067,0.02489051,0.01006545,0.5378079],"study_design_scores_gemma":[0.00008895407,0.0002237647,0.001139556,0.00003051024,0.00007927963,0.0001512317,0.0000740381,0.8963398,0.05142066,0.02787774,0.02254411,0.00003030623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1253713,0.004383279,0.8383706,0.001491136,0.0003463267,0.0001742517,0.0001398471,0.004322675,0.02540058],"genre_scores_gemma":[0.832643,0.002482281,0.1544284,0.0003066128,0.0003055517,0.0001333435,0.0002170707,0.0004999891,0.008983778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002857963,"threshold_uncertainty_score":0.009560823,"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."}}