{"id":"W2125953909","doi":"10.1109/22.898983","title":"Efficient sensitivity analysis of transmission-line networks using model-reduction techniques","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":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Sensitivity (control systems); Reduction (mathematics); Transmission line; Model order reduction; Waveform; Electric power transmission; Lossy compression; Electronic engineering; Algorithm; Subspace topology; Voltage; Transmission (telecommunications); Computer science; Transformation (genetics); Mathematics; Topology (electrical circuits); Engineering; Telecommunications; Mathematical analysis; Electrical engineering","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":[0.0007695694,0.000964571,0.0006398972,0.0006236971,0.0002986883,0.0005679849,0.000494994,0.0004770135,0.001914687],"category_scores_gemma":[0.002454426,0.0003970721,0.0006190968,0.0003919011,0.000521869,0.0008311479,0.0006499426,0.0007098051,0.0002825395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005982364,"about_ca_system_score_gemma":0.0005383645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00179613,"about_ca_topic_score_gemma":0.001565601,"domain_scores_codex":[0.9996282,0.0001652092,0.00001120656,0.00004102613,0.0001281575,0.00002627754],"domain_scores_gemma":[0.9991575,0.0006613458,0.00003706336,0.00005522029,0.00007839934,0.00001052753],"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":[0.00002705423,0.00002224906,0.0002078626,0.00003998744,0.00002511311,0.00003895912,0.00003018197,0.9569812,0.006618344,0.009860918,0.0002279447,0.02592026],"study_design_scores_gemma":[0.000001756714,0.00000512778,0.00003426947,0.000001516823,0.000001876225,0.000006869585,0.00000198145,0.9956568,0.0009468377,0.003218364,0.0001221293,0.000002495225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008738217,0.00003851065,0.99006,0.0000330642,0.000004437025,0.00002174804,0.00002435159,0.0002861464,0.0007935993],"genre_scores_gemma":[0.6506806,0.00022914,0.3464458,0.00004547132,0.00001710716,0.0002700063,0.000185605,0.0001975175,0.001928735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001914687,"threshold_uncertainty_score":0.006405234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391020846522623,"score_gpt":0.264475557921662,"score_spread":0.2505653494564358,"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."}}