{"id":"W4388733895","doi":"10.1002/mop.33954","title":"Padé via Arnoldi with single‐size matrix simplification for electromagnetic fast frequency sweep","year":2023,"lang":"en","type":"article","venue":"Microwave and Optical Technology Letters","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; Central China Normal University; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Lanczos resampling; Matrix (chemical analysis); Algorithm; Microwave; Lanczos algorithm; Mathematics; Computer science; Mathematical optimization; Physics; Eigenvalues and eigenvectors; Materials science; 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":[0.00009992968,0.0002040849,0.0002388441,0.0002184617,0.0000803622,0.00003014385,0.0001331911,0.0002091501,0.00001107307],"category_scores_gemma":[0.00008673023,0.0001879589,0.00004270634,0.0007523567,0.0002029747,0.00004471286,0.00002152744,0.0002404003,0.00002877149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004313462,"about_ca_system_score_gemma":0.000005600482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001322632,"about_ca_topic_score_gemma":0.000002212129,"domain_scores_codex":[0.9988434,0.000017125,0.0002309004,0.0003091848,0.0000848984,0.0005145283],"domain_scores_gemma":[0.9993277,0.0003090443,0.0000283442,0.0002279915,0.00003287963,0.00007402133],"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":[0.0000155612,0.00001504022,0.000144986,0.00003459611,0.00002949959,0.000008307805,0.00002268359,0.00009880195,0.9603119,0.001419023,0.0002932414,0.03760634],"study_design_scores_gemma":[0.002248287,0.002553663,0.005978312,0.00005434553,0.0001806684,0.0001961549,0.0001098622,0.03099523,0.9359116,0.01803403,0.002636031,0.001101841],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7835533,0.0002057117,0.2089023,0.005481367,0.00005263106,0.0003097685,0.000002749084,0.001262133,0.0002300265],"genre_scores_gemma":[0.8825839,0.00003516685,0.116768,0.000368775,0.00004146927,0.00009341856,0.0000128119,0.00004824241,0.00004818105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09903059,"threshold_uncertainty_score":0.7664743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007643470757193628,"score_gpt":0.2333573821761265,"score_spread":0.2257139114189329,"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."}}