{"id":"W2144414501","doi":"10.1109/6040.861551","title":"Efficient passive circuit models for distributed networks with frequency-dependent parameters","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Advanced Packaging","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Padé approximant; Transmission line; Electronic engineering; Dependency (UML); Computer science; Exponential function; Equivalent circuit; Network analysis; Electric power transmission; Transient (computer programming); Transmission (telecommunications); Reduction (mathematics); Frequency response; Algorithm; Mathematics; Engineering; Voltage; Electrical engineering; Telecommunications; Applied mathematics","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":[0.0002626437,0.0009348044,0.000537249,0.0004008542,0.0002785338,0.0005457818,0.001230075,0.0007501041,0.002448154],"category_scores_gemma":[0.0008186836,0.0004136379,0.000608346,0.0003226647,0.0003567949,0.001461931,0.0004018859,0.0009975624,0.001102636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503324,"about_ca_system_score_gemma":0.0003885516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008908691,"about_ca_topic_score_gemma":0.002115775,"domain_scores_codex":[0.9998524,0.00003532528,0.000005581053,0.00002407129,0.00007323716,0.000009430561],"domain_scores_gemma":[0.9998156,0.0001004163,0.00002089664,0.00002779614,0.00003088653,0.000004462043],"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":[0.00002485585,0.0000376973,0.0001387103,0.0001220981,0.00002727833,0.00006993975,0.00007427489,0.8882458,0.01331519,0.05031015,0.000968096,0.04666609],"study_design_scores_gemma":[0.000004007145,0.000009721762,0.00002501599,0.000005545033,0.000005765339,0.00002979658,0.000005150957,0.9881968,0.00147592,0.007599105,0.002639619,0.000003485621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001774476,0.00007881544,0.9968758,0.00002300205,0.000008179217,0.00001235759,0.00002651882,0.0001690111,0.001031839],"genre_scores_gemma":[0.3128443,0.001108979,0.6668398,0.00008885986,0.00006256891,0.0004820363,0.0004387522,0.0003639594,0.01777075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002448154,"threshold_uncertainty_score":0.008189857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537294796279468,"score_gpt":0.2324645694749876,"score_spread":0.2170916215121929,"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."}}