{"id":"W2146661194","doi":"10.1109/tadvp.2008.2011369","title":"Passive Macromodeling of Lossy Multiconductor Transmission Lines Based on the Method of Characteristics","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Advanced Packaging","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Passivity; Discretization; Lossy compression; Transmission line; Electric power transmission; Computer science; Electronic engineering; Algorithm; Key (lock); Transmission (telecommunications); Topology (electrical circuits); Control theory (sociology); Mathematics; Engineering; Mathematical analysis; Electrical engineering; Telecommunications","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":[0.0001127707,0.000180766,0.0002681468,0.00009308002,0.0001448827,0.0000114784,0.0001342585,0.00002954827,0.0001120206],"category_scores_gemma":[0.000002939935,0.0001322685,0.0001436451,0.000185662,0.00003565206,0.00006718496,3.882461e-7,0.0002531498,0.000002436774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001762524,"about_ca_system_score_gemma":0.00003925194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001097644,"about_ca_topic_score_gemma":1.696972e-7,"domain_scores_codex":[0.9989918,0.00007595779,0.0003296779,0.0002096039,0.0001738157,0.000219072],"domain_scores_gemma":[0.9991229,0.0002868846,0.0001673331,0.0002796086,0.00008562445,0.00005765667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001337118,0.0003863505,0.00001681014,0.00002238983,0.00003735244,5.127476e-7,0.0006319646,0.07834331,0.5213423,0.0004063694,0.000004285931,0.3986746],"study_design_scores_gemma":[0.0006865124,0.0003350909,0.0001121055,0.0002265056,0.00006196337,4.326544e-7,0.0001800008,0.1436034,0.853161,0.001400659,0.00006465065,0.0001676127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.173679,0.000008070614,0.8250012,0.0005882696,0.00009886798,0.0001552861,0.00004985886,0.00002286872,0.0003965106],"genre_scores_gemma":[0.9776555,0.000004637493,0.02207039,0.0001082304,0.00004452367,0.00001001994,0.000005583924,0.00001706521,0.00008404296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8039765,"threshold_uncertainty_score":0.5393753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162911181019896,"score_gpt":0.266391783877631,"score_spread":0.254762672067432,"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."}}