{"id":"W2100354616","doi":"10.1109/22.898981","title":"Passive model reduction of multiport distributed interconnects","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Transmission line; Reduction (mathematics); Padé approximant; Electronic engineering; Telegrapher's equations; Signal integrity; Lossy compression; Computer science; Model order reduction; Distributed element model; A priori and a posteriori; Matrix (chemical analysis); Exponential function; Equivalent circuit; Topology (electrical circuits); Interconnection; Mathematics; Algorithm; Engineering; Electrical engineering; Applied mathematics; Mathematical analysis; Telecommunications; Voltage","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":[0.0002382449,0.0005499793,0.0004519293,0.0002265793,0.0002434551,0.0004897499,0.0007714134,0.0005136264,0.001320143],"category_scores_gemma":[0.0006521667,0.0002573254,0.0005663011,0.0002172517,0.0003988367,0.001123074,0.0005710987,0.0007807516,0.0004081779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004226445,"about_ca_system_score_gemma":0.0004010392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009258691,"about_ca_topic_score_gemma":0.001352916,"domain_scores_codex":[0.9998335,0.00004249956,0.000006059927,0.00003215156,0.00007411595,0.00001167586],"domain_scores_gemma":[0.9998283,0.00005924712,0.00002454642,0.00004773132,0.00003489721,0.00000519927],"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":[0.00002838967,0.00003139595,0.0002665115,0.00005068405,0.00002364538,0.0000635189,0.00007597107,0.909391,0.01417707,0.04151996,0.0006492782,0.03372263],"study_design_scores_gemma":[0.000002103269,0.00001029083,0.00002980729,0.000001374823,0.000002703076,0.0000122357,0.000004303417,0.9914962,0.001882443,0.005723746,0.000832843,0.000001978171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01309086,0.00005874416,0.9848161,0.00006670923,0.00001417672,0.00000967399,0.00002522943,0.0002803078,0.001638166],"genre_scores_gemma":[0.7559673,0.0003483084,0.2300065,0.00008341071,0.00003873343,0.0001503242,0.0002773609,0.0002288846,0.01289919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001320143,"threshold_uncertainty_score":0.004416347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009789483528280525,"score_gpt":0.2423544902244515,"score_spread":0.232565006696171,"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."}}