{"id":"W1907317439","doi":"10.1109/ursi-at-rasc.2015.7302940","title":"Large-scale high-order 3D electromagnetic analysis with Locally Corrected Nystrom discretization of Combined Field Integral Equation","year":2015,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Discretization; Integral equation; Scale (ratio); Nyström method; Electric-field integral equation; Computational electromagnetics; Field (mathematics); Order (exchange); Computer science; Electromagnetic field; Applied mathematics; Mathematics; Mathematical analysis; Physics","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":[0.0003959655,0.0004711593,0.0006185189,0.0003656209,0.0002626655,0.0008609322,0.0008982119,0.0008675948,0.002170305],"category_scores_gemma":[0.0008630814,0.0002963225,0.0006582164,0.0003285946,0.0005645814,0.0006380253,0.0006928167,0.0009670748,0.0004973225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006457157,"about_ca_system_score_gemma":0.001050816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006572196,"about_ca_topic_score_gemma":0.006158476,"domain_scores_codex":[0.9997899,0.00006008577,0.000009146783,0.00002113996,0.0001017049,0.00001809538],"domain_scores_gemma":[0.9996907,0.000129764,0.00004673762,0.00003874559,0.00007460265,0.00001956286],"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":[0.00003260731,0.00003363141,0.000946609,0.00008881102,0.00001963112,0.0001491593,0.0001213021,0.9480284,0.007115358,0.03126646,0.00077313,0.0114248],"study_design_scores_gemma":[0.000002658023,0.000004920839,0.00004734579,0.000004117192,0.00000126009,0.0000153864,0.000007642914,0.9973819,0.0003803338,0.001195702,0.0009564008,0.000002470656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01739056,0.0001571654,0.9757574,0.0001107306,0.00004380706,0.00003591719,0.0001058958,0.0001936015,0.006204988],"genre_scores_gemma":[0.5078152,0.0005137651,0.4757338,0.0001318,0.00006743702,0.0003193942,0.0004833285,0.0002814414,0.01465378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006572196,"threshold_uncertainty_score":0.01306784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005094604531340749,"score_gpt":0.2090385234674184,"score_spread":0.2039439189360777,"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."}}