{"id":"W16951981","doi":"10.1007/0-387-28967-4_8","title":"Energy Aware Scheduling for Heterogeneous Real-Time Embedded Systems Using Genetics Algorithms","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Scheduling (production processes); Energy consumption; Dynamic voltage scaling; Genetic algorithm; Distributed computing; Real-time computing; Algorithm; Dynamic priority scheduling; Simulated annealing; Mathematical optimization; Engineering; Schedule; Mathematics; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002351443,0.0005537386,0.000553703,0.0002946662,0.0003164502,0.0005004355,0.0008275065,0.0005320893,0.001726222],"category_scores_gemma":[0.0005895619,0.0002763288,0.0005052331,0.000478425,0.0004285531,0.0005592472,0.0003594178,0.000851654,0.0002628814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006529708,"about_ca_system_score_gemma":0.0004977325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002199724,"about_ca_topic_score_gemma":0.003400988,"domain_scores_codex":[0.9999245,0.00001781028,0.000003089164,0.00001472926,0.0000300897,0.000009798609],"domain_scores_gemma":[0.9998616,0.00008396736,0.00001036098,0.00001435393,0.00002180363,0.000007825024],"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":[0.00002905794,0.00003228213,0.0002044826,0.00004929426,0.00003546871,0.00004175546,0.00004054236,0.9099174,0.007872876,0.023491,0.001455516,0.05683031],"study_design_scores_gemma":[0.0000140691,0.00001656565,0.0001157324,0.000004975328,0.000009136274,0.00002000574,0.000009983397,0.9789971,0.001103365,0.01829851,0.001405544,0.000005021509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02731985,0.001112544,0.9634264,0.0002647021,0.0001159353,0.00003057923,0.00003010638,0.0005670789,0.007132802],"genre_scores_gemma":[0.4025248,0.0012974,0.5855691,0.0001330929,0.00008401691,0.000134198,0.0001204455,0.0003317936,0.009805128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002199724,"threshold_uncertainty_score":0.005774736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381436979837589,"score_gpt":0.2733660332818529,"score_spread":0.239551663483477,"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."}}