{"id":"W2594730346","doi":"","title":"A decomposition method using FEM for long waveguides","year":2006,"lang":"en","type":"article","venue":"International Symposium on Antenna Technology and Applied Electromagnetics","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Finite element method; Physical optics; Method of moments (probability theory); Computational electromagnetics; Geometrical optics; Decomposition; Diffraction; Uniform theory of diffraction; Integral equation; Decomposition method (queueing theory); Electromagnetics; Computer science; Mathematical analysis; Applied mathematics; Mathematics; Optics; Physics; Electromagnetic field; Electronic engineering; Engineering; Structural engineering","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":[0.0002843862,0.0005158989,0.0003796785,0.0003504152,0.0003411623,0.0005425037,0.0004092779,0.0005986822,0.004402911],"category_scores_gemma":[0.000525753,0.0002443409,0.0006085252,0.0003916257,0.0003794558,0.0006560642,0.0006335226,0.001087543,0.001698587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000248674,"about_ca_system_score_gemma":0.0005423866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008052537,"about_ca_topic_score_gemma":0.0006575878,"domain_scores_codex":[0.9998201,0.00005315161,0.000007457244,0.00002135696,0.00008730952,0.0000105992],"domain_scores_gemma":[0.9998618,0.00005047707,0.00001049179,0.00001899338,0.00004811161,0.000009980634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006070134,0.0000728157,0.0004014519,0.0003858118,0.0000488279,0.0002435632,0.0002259598,0.2857307,0.06293979,0.352714,0.01273584,0.2844405],"study_design_scores_gemma":[0.000009091039,0.000036366,0.0001381425,0.00005626999,0.00001004417,0.0001873403,0.00002808866,0.9246722,0.004749203,0.02556743,0.04452738,0.0000183391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008635416,0.0002025125,0.9960251,0.00005286354,0.00006697512,0.00001602377,0.00002717312,0.0001546379,0.002591137],"genre_scores_gemma":[0.03813296,0.0008773174,0.9509892,0.0001228999,0.00007868247,0.0001923085,0.00012066,0.0001545724,0.009331402],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004402911,"threshold_uncertainty_score":0.0147292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005955810895152594,"score_gpt":0.2432891818665,"score_spread":0.2373333709713474,"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."}}