{"id":"W2144538531","doi":"10.1109/tmag.2007.916126","title":"An Efficient High-Order Extrapolation Procedure for Multiaspect Electromagnetic Scattering Analysis","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Extrapolation; Method of moments (probability theory); Radar cross-section; Computational electromagnetics; Scattering; Computer science; Electromagnetic field; Physical optics; Waveform; Physics; Field (mathematics); Computational physics; Radar; Applied mathematics; Mathematical analysis; Optics; Mathematics; Telecommunications; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009597143,0.0003107951,0.0003536369,0.0004642399,0.0005156701,0.00006476673,0.0002138023,0.0000743687,0.0008464623],"category_scores_gemma":[0.000001543882,0.00032221,0.0003403093,0.001232816,0.00008945485,0.00007062244,9.901325e-7,0.0002185607,0.00002401741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003959887,"about_ca_system_score_gemma":0.00006355939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002274453,"about_ca_topic_score_gemma":0.0000432905,"domain_scores_codex":[0.9982535,0.00005502344,0.0003712191,0.0005580471,0.0002658318,0.0004964405],"domain_scores_gemma":[0.9990125,0.00008273561,0.0001068497,0.0004790824,0.0001617919,0.0001570261],"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":[0.0001062076,0.001774149,0.002055049,0.00003541731,0.00104713,0.000003454223,0.0008634671,0.7706609,0.1930857,0.0001735743,0.00008309799,0.03011183],"study_design_scores_gemma":[0.002244514,0.003061351,0.02676763,0.00002468741,0.00381936,0.00001180748,0.0002367568,0.8251468,0.1371178,0.0002940801,0.00009606445,0.001179183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5091937,0.00002080431,0.4902177,0.00009513625,0.00006614225,0.0002088173,0.0000411692,0.00006858648,0.00008800675],"genre_scores_gemma":[0.9843928,0.00001053594,0.01423477,0.00005593829,0.0001566049,0.0001703468,0.00006228845,0.00004672458,0.0008700244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4759829,"threshold_uncertainty_score":0.999923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009086238144553439,"score_gpt":0.2394753061507519,"score_spread":0.2303890680061985,"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."}}