{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009277037,0.0002053821,0.0001748837,0.0003134374,0.00008581593,0.00004321778,0.0001623207,0.0002021897,0.000005818294],"category_scores_gemma":[0.000006403225,0.0002198592,0.00004676892,0.0001885396,0.00005460221,0.00003180373,0.00002487826,0.0001807087,0.000004475248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007834962,"about_ca_system_score_gemma":0.000005939277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003413879,"about_ca_topic_score_gemma":0.000004690831,"domain_scores_codex":[0.9991097,0.00000454747,0.0002507649,0.0002467406,0.00009311498,0.0002950878],"domain_scores_gemma":[0.9996942,0.00005789996,0.0000369003,0.0001277417,0.00005382195,0.00002944978],"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":[0.00003254113,0.00003254354,0.00007359528,0.00002425241,0.0000475301,0.000003308765,0.00000690482,0.01147541,0.9324961,0.053779,0.0001991919,0.001829609],"study_design_scores_gemma":[0.0007168427,0.000187593,0.000446882,0.00004856938,0.00004468314,0.0001802358,0.00001307946,0.4788904,0.5041604,0.01216233,0.002757713,0.0003912485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4430766,0.0003155299,0.5516633,0.0006106274,0.000241341,0.0002426257,0.00001797725,0.0006123834,0.003219604],"genre_scores_gemma":[0.9530972,0.00007210349,0.04635138,0.00005918325,0.0001530604,0.00004526012,0.00003865955,0.00004263172,0.0001405334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5100206,"threshold_uncertainty_score":0.8965597,"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."}}