{"id":"W2125663622","doi":"10.1109/ccece.2006.277531","title":"Efficient Macromodel for Interconnects Excited by Incident Fields","year":2006,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Krylov subspace; Reduction (mathematics); Model order reduction; Computer science; Interconnection; Projection (relational algebra); Transformation (genetics); Macro; Singular value decomposition; Computational electromagnetics; Electronic engineering; Subspace topology; Process (computing); Electromagnetic field; Computational science; Algorithm; Iterative method; Mathematics; Engineering; Telecommunications; Physics","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":[0.0002453784,0.0004718411,0.0005317073,0.000269975,0.0002505408,0.0003842787,0.0005782336,0.000595874,0.001973834],"category_scores_gemma":[0.0004497296,0.0002523909,0.00047739,0.0002652146,0.0002776993,0.0005323419,0.000335633,0.0006879646,0.0008645411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002563191,"about_ca_system_score_gemma":0.0006479779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144547,"about_ca_topic_score_gemma":0.001582902,"domain_scores_codex":[0.9998295,0.00004414692,0.000005488305,0.0000179076,0.00009055251,0.00001236429],"domain_scores_gemma":[0.9998705,0.00004845217,0.00001535218,0.00003440629,0.00002535892,0.000005829653],"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":[0.00004835579,0.00003936265,0.0002741476,0.00008553291,0.00001487937,0.00009007897,0.0001163225,0.8797134,0.05198652,0.02299823,0.0006591317,0.04397402],"study_design_scores_gemma":[0.000005795028,0.00001737989,0.00004306448,0.000002331823,0.000002993549,0.00001980194,0.000007673571,0.9910006,0.004597059,0.002309612,0.001990198,0.000003499654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009705393,0.00002890573,0.9884144,0.00003034129,0.000009328885,0.00001832927,0.00003215552,0.000380083,0.001381009],"genre_scores_gemma":[0.3824517,0.0002527205,0.6065855,0.00004038079,0.00002981579,0.0002812916,0.0004692909,0.0002766372,0.009612693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001973834,"threshold_uncertainty_score":0.006603122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004212195722910705,"score_gpt":0.1945478156003181,"score_spread":0.1903356198774074,"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."}}