{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001209857,0.0001524915,0.0001720952,0.00006962712,0.0001184,0.0000133916,0.00006610084,0.00006623367,0.0006579519],"category_scores_gemma":[3.970309e-7,0.0001400749,0.0001067934,0.00009811669,0.0001368103,0.0001263679,8.699849e-7,0.0002176755,0.000004377445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001254798,"about_ca_system_score_gemma":0.00001455903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001562745,"about_ca_topic_score_gemma":6.689021e-7,"domain_scores_codex":[0.9992851,0.00007998216,0.0002178905,0.0002186472,0.00005996029,0.0001384176],"domain_scores_gemma":[0.9996288,0.00003747276,0.00006413475,0.000170302,0.00004185547,0.00005744992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004867709,0.0002963251,0.000001777332,0.00001210265,0.00007793839,6.436363e-7,0.0002935297,0.004441088,0.3218175,0.01476517,0.0002555273,0.6575516],"study_design_scores_gemma":[0.0001771972,0.00008394262,9.755814e-7,0.00004820329,0.00003905106,0.000009086434,0.0001130993,0.001460992,0.9629308,0.03476654,0.0002294217,0.0001406801],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3362185,0.000008943513,0.6613459,0.00003960343,0.0000455479,0.0001729199,0.00007522282,0.00007885471,0.002014481],"genre_scores_gemma":[0.997059,0.0000677329,0.001403718,0.00002664443,0.00004685524,0.00005302946,0.00001552045,0.00001588402,0.001311545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6608405,"threshold_uncertainty_score":0.7204111,"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."}}