{"id":"W1988198042","doi":"10.1109/tcpmt.2012.2204393","title":"Multiorder Arnoldi Approach for Model Order Reduction of PEEC Models With Retardation","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Components Packaging and Manufacturing Technology","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Model order reduction; Reduction (mathematics); Arnoldi iteration; Order (exchange); Computer science; Generalized minimal residual method; Mathematics; Mathematical optimization; Economics; Algorithm; Iterative method; Finance","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":[0.00009726024,0.0001822207,0.0002121533,0.0003204605,0.000136525,0.000008716399,0.00008462459,0.0001461365,0.000003318072],"category_scores_gemma":[0.000001210014,0.000168236,0.00003567061,0.0001098858,0.0000809591,0.0002061909,0.000002114681,0.0002505106,4.64604e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003963928,"about_ca_system_score_gemma":0.000007054969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001289601,"about_ca_topic_score_gemma":0.000002475648,"domain_scores_codex":[0.9991623,0.00001398523,0.0002001116,0.0002163979,0.0001094365,0.0002977663],"domain_scores_gemma":[0.9995748,0.00002509379,0.0000433072,0.0002657524,0.00003907203,0.00005193202],"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":[0.00009980219,0.0001951294,0.00001438654,0.0001782093,0.00006264509,7.65365e-8,0.0003234059,0.8992931,0.07597357,0.0001062831,0.0000159006,0.02373745],"study_design_scores_gemma":[0.0004744453,0.0000796421,0.00003786752,0.00003447627,0.0000424499,0.00001568806,0.00006466852,0.5653707,0.4331256,0.0006031224,0.00001702926,0.0001343074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4938349,0.00005072261,0.5055186,0.00003532575,0.00006849233,0.0001984361,0.000007187108,0.0002302095,0.00005616557],"genre_scores_gemma":[0.9311522,0.00005037243,0.06860512,0.00000343413,0.00001283509,0.0000901146,0.00001216959,0.00003060987,0.00004319048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4373173,"threshold_uncertainty_score":0.6860465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019192933738268,"score_gpt":0.2177242900132882,"score_spread":0.1985313562750202,"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."}}