{"id":"W2060215587","doi":"10.1109/tnano.2015.2408353","title":"Time and Frequency Domain Analysis of MLGNR Interconnects","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Nanotechnology","topic":"Graphene research and applications","field":"Materials Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Interconnection; Graphene; Capacitance; Materials science; Bandwidth (computing); Dissipation; Graphene nanoribbons; Doping; Optoelectronics; Conductor; Computer science; Electronic engineering; Nanotechnology; Physics; Telecommunications; Engineering; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002314807,0.00009025182,0.0002349911,0.000724319,0.00007130396,0.00001125003,0.0002143185,0.0001414419,0.0002313898],"category_scores_gemma":[0.00001582965,0.00008183961,0.00007036289,0.001152898,0.0004081172,0.00007055994,0.000002894105,0.0001305311,0.0001223482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003150258,"about_ca_system_score_gemma":0.00004890278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001637576,"about_ca_topic_score_gemma":0.0002707088,"domain_scores_codex":[0.9991538,0.0000523503,0.0001945559,0.0002490938,0.0001443836,0.0002058016],"domain_scores_gemma":[0.9993262,0.00007956258,0.00005485247,0.0003605518,0.00008328183,0.00009557807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002111742,0.0001094581,0.000009465357,0.000003630718,0.00009587006,0.000002095728,0.0001323064,0.0003680456,0.994509,0.002611188,0.00004052547,0.002097306],"study_design_scores_gemma":[0.0003791365,0.0003173506,0.00003908633,0.000008230653,0.0001678661,0.000009302825,0.0002371059,0.0009013574,0.9833429,0.01434192,0.0001336792,0.0001220354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8557691,0.00005330449,0.1429288,0.0007294851,0.00004432983,0.0001165935,0.00008058854,0.0001080133,0.0001697962],"genre_scores_gemma":[0.9959621,0.00002234859,0.003865094,0.00002647696,0.000003513815,0.00004909498,0.000002456086,0.000007056422,0.0000617968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1401931,"threshold_uncertainty_score":0.3337323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860782620636699,"score_gpt":0.2671955977995986,"score_spread":0.2485877715932316,"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."}}