{"id":"W2163753693","doi":"10.1109/spi.2005.1500943","title":"Time domain reduced order macromodel for interconnects excited by incident fields","year":2005,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; McGill University","funders":"","keywords":"Krylov subspace; Spice; Computer science; Model order reduction; Projection (relational algebra); Time domain; Reduction (mathematics); Electronic engineering; Frequency domain; Subspace topology; Equivalent circuit; Order (exchange); Topology (electrical circuits); Algorithm; Mathematics; Engineering; Electrical engineering; Iterative method","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001145361,0.0001144618,0.0001382684,0.0000453639,0.0000267889,0.00001883849,0.0000899832,0.00008885054,0.001384709],"category_scores_gemma":[0.0000637976,0.0001066159,0.00004462185,0.0001415923,0.00001261545,0.00006279788,0.00001287188,0.0000836188,0.00007275693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004325802,"about_ca_system_score_gemma":0.000005803952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005308873,"about_ca_topic_score_gemma":0.000004730177,"domain_scores_codex":[0.9993587,0.00002409257,0.0001893245,0.0001332471,0.00007287611,0.0002218277],"domain_scores_gemma":[0.999559,0.000183612,0.00001382451,0.0001300088,0.00003588665,0.00007768566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006745246,0.00009548302,0.000007085384,0.00004002371,0.00007846228,0.000001156484,0.0006675346,0.05642214,0.6097944,0.000802337,0.1991184,0.1329055],"study_design_scores_gemma":[0.0006431105,0.0001220628,0.00002322941,0.000006239958,0.000007272361,0.000003238094,0.0000153456,0.9251854,0.04143082,0.001237302,0.03112463,0.0002013696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4052404,0.0001068819,0.5840419,0.0009839181,0.00008010858,0.0002049484,0.000003150177,0.0003160794,0.009022648],"genre_scores_gemma":[0.8378851,0.000008114757,0.1550783,0.0007570782,0.00009363823,0.00004227475,0.0000172073,0.00003158093,0.006086736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8687633,"threshold_uncertainty_score":0.9995282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009076530966964412,"score_gpt":0.2638227506253075,"score_spread":0.2547462196583431,"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."}}