{"id":"W2467266971","doi":"10.1109/tcad.2016.2589899","title":"Fast Vectorless RLC Grid Verification","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RLC circuit; Speedup; Computer science; Grid; Computation; Scalability; Process (computing); Matrix (chemical analysis); Power (physics); Computer engineering; Algorithm; Electronic engineering; Voltage; Parallel computing; Electrical engineering; Mathematics; Capacitor; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0003237598,0.0003547505,0.0004690332,0.0003872595,0.0001145106,0.00008702168,0.0002510778,0.0002023642,0.00002878864],"category_scores_gemma":[0.000003014062,0.0002570727,0.00008641535,0.0004171378,0.00008693992,0.0003356758,8.593591e-7,0.0002172145,0.00006797467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001869289,"about_ca_system_score_gemma":0.0000607088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005461172,"about_ca_topic_score_gemma":0.000003242723,"domain_scores_codex":[0.9982076,0.0001517738,0.0006437182,0.0003677491,0.0002889812,0.000340178],"domain_scores_gemma":[0.998862,0.0002406746,0.0001099888,0.0004397933,0.0002041798,0.0001433795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005345224,0.0001589512,0.00002879255,0.0002983967,0.0003371221,0.00001186564,0.000562719,0.3629231,0.418488,0.0002735419,0.001677966,0.2151861],"study_design_scores_gemma":[0.002205712,0.0009247645,0.0003801172,0.001751734,0.0001092847,0.0001337812,0.000186198,0.7126688,0.2793294,0.00002880287,0.001314776,0.0009665916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04652937,0.0002201645,0.9483818,0.00001525559,0.003651242,0.0005316593,0.0001014249,0.000442118,0.0001269209],"genre_scores_gemma":[0.9986776,0.00026894,0.0006041497,0.00001092593,0.0001455431,0.00009245605,0.0000046148,0.00006432262,0.0001314106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9521483,"threshold_uncertainty_score":0.9999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124078053858001,"score_gpt":0.1994336642177718,"score_spread":0.1781928836791918,"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."}}