{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005982141,0.00009429649,0.00009761615,0.00003436623,0.00003374457,0.00001789121,0.00008187367,0.00006029801,0.0001808749],"category_scores_gemma":[0.000009897307,0.00008510068,0.00004671607,0.00005959424,0.000009505253,0.0000167255,0.00001809437,0.00006605169,0.000009279479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003240399,"about_ca_system_score_gemma":0.000003974198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001127847,"about_ca_topic_score_gemma":0.0001559016,"domain_scores_codex":[0.9994394,0.000007779398,0.0001574076,0.0001288425,0.00007282624,0.000193743],"domain_scores_gemma":[0.9997166,0.00007706152,0.000009306078,0.000143454,0.00002087667,0.00003273021],"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":[0.00005018862,0.000219543,0.0001594299,0.0001859368,0.0000196385,0.000002389658,0.000241703,0.3393994,0.3687306,0.001397487,0.283386,0.006207676],"study_design_scores_gemma":[0.0003332743,0.00008198778,0.0002360932,0.00001435241,0.000005481901,0.000001659027,0.000009715683,0.9016973,0.09468757,0.001159554,0.001647412,0.0001256214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8856402,0.0002276779,0.1084255,0.00009785168,0.0001163987,0.0001948296,0.000007931066,0.000224114,0.005065467],"genre_scores_gemma":[0.9978603,0.000002261884,0.00107201,0.00004129946,0.0000378864,0.00003354051,0.00002731546,0.00001306999,0.0009123204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5622979,"threshold_uncertainty_score":0.3470305,"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."}}