{"id":"W2901666864","doi":"10.1109/tvlsi.2018.2877107","title":"Power Scheduling With Active &lt;inline-formula&gt; &lt;tex-math notation=\"LaTeX\"&gt;$RC$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Power Grids","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voltage; Engineering; Power gating; Chip; Electronic engineering; Voltage drop; Electrical engineering; Computer science; Transistor","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001354279,0.001913791,0.001689161,0.001589985,0.001357491,0.0007744823,0.001042664,0.001220932,0.0004770161],"category_scores_gemma":[0.00006422475,0.001664713,0.0007139135,0.002365371,0.0003817135,0.003976395,0.00001969792,0.001731565,0.002544368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001618744,"about_ca_system_score_gemma":0.0004671213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006408385,"about_ca_topic_score_gemma":0.001006153,"domain_scores_codex":[0.9912499,0.0003119704,0.002502287,0.001706998,0.002190608,0.002038197],"domain_scores_gemma":[0.9945139,0.000381818,0.0006630988,0.001946653,0.00176364,0.0007308891],"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":[0.002977039,0.003743398,0.0002041603,0.001488984,0.004048821,0.0002302146,0.02918528,0.6011102,0.326116,0.01409759,0.006203548,0.01059478],"study_design_scores_gemma":[0.005347122,0.001947415,0.0005831348,0.002146116,0.0005184465,0.0003728126,0.001800489,0.8325545,0.115553,0.00006056593,0.03592612,0.003190309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4669217,0.0003315702,0.5186973,0.0001193696,0.006728486,0.001861666,0.0006945464,0.001564848,0.003080564],"genre_scores_gemma":[0.9897102,0.0002143424,0.004591458,0.0001857484,0.0009356322,0.0007434631,0.0003017557,0.0005417972,0.002775599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5227886,"threshold_uncertainty_score":0.9999426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00797039183236096,"score_gpt":0.2179120784255335,"score_spread":0.2099416865931725,"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."}}