{"id":"W2166532433","doi":"10.1109/rtcsa.2009.25","title":"Integrating Preemption Threshold to Fixed Priority DVS Scheduling Algorithms","year":2009,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Preemption; Computer science; Dynamic voltage scaling; Energy consumption; Scheduling (production processes); Algorithm; Embedded system; Real-time computing; Parallel computing; Operating system; Mathematical optimization; Mathematics; 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":[0.001318145,0.0005380474,0.0006227274,0.0008812136,0.0004854891,0.001061814,0.001994633,0.0003978365,0.0007863891],"category_scores_gemma":[0.00430871,0.0002611953,0.0003333226,0.0008881011,0.0004556137,0.0009614116,0.0005791953,0.0008318753,0.0001772325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063906,"about_ca_system_score_gemma":0.001639635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002120804,"about_ca_topic_score_gemma":0.002272455,"domain_scores_codex":[0.9989187,0.0002466579,0.0001193284,0.0001656133,0.0004466198,0.0001031664],"domain_scores_gemma":[0.9977302,0.000822853,0.0002572889,0.0003865834,0.0006123202,0.0001907965],"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":[0.0008374104,0.0005219564,0.005487735,0.000297777,0.0001201638,0.0001678259,0.0002288913,0.1696841,0.07726977,0.01894928,0.002713247,0.7237218],"study_design_scores_gemma":[0.0001198612,0.0003401455,0.0009373169,0.00002138221,0.00005051498,0.0002615306,0.00005487262,0.9407394,0.04344471,0.007125226,0.006879157,0.00002591586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08352246,0.00110251,0.9106877,0.0002663055,0.000201261,0.0001063784,0.00002941355,0.00138134,0.002702616],"genre_scores_gemma":[0.5505185,0.0003376737,0.4475054,0.0001026894,0.00006495829,0.00005080976,0.00006644941,0.0001016,0.00125195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002120804,"threshold_uncertainty_score":0.007719159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02146545021472751,"score_gpt":0.2912733675942191,"score_spread":0.2698079173794916,"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."}}