{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008455948,0.0002045018,0.0001026364,0.0002691864,0.00009770477,0.0002337662,0.0001537968,0.0002793582,0.001374413],"category_scores_gemma":[0.0004142895,0.00006375444,0.0001546719,0.0002054478,0.0001226719,0.000234998,0.00008263132,0.0001793264,0.0002095677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138048,"about_ca_system_score_gemma":0.0001035544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581394,"about_ca_topic_score_gemma":0.001061874,"domain_scores_codex":[0.999947,0.000008045836,0.000001810355,0.00001086625,0.00002331059,0.000008988695],"domain_scores_gemma":[0.9998549,0.00005831389,0.00003321025,0.0000152032,0.00003240881,0.00000591607],"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":[0.0002614629,0.00006674827,0.004076875,0.0001945048,0.00005063675,0.0009499281,0.0002715922,0.7752084,0.1502725,0.02074181,0.001167114,0.04673847],"study_design_scores_gemma":[0.000001497369,0.00002559851,0.001109897,0.000003737115,0.000002885435,0.00006097392,0.00001920579,0.9945898,0.003158167,0.0004757246,0.0005487666,0.000003696346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7515866,0.0005502186,0.2333272,0.0002378034,0.00003776058,0.00002710311,0.0003464936,0.0006434861,0.01324326],"genre_scores_gemma":[0.9866785,0.0001426391,0.01070622,0.00001780813,0.000008736896,0.00001132423,0.0001163426,0.00002637829,0.002292035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001581394,"threshold_uncertainty_score":0.004597902,"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."}}