{"id":"W4212990880","doi":"10.1109/aps/ursi47566.2021.9703851","title":"On Evaluation of Incident Fields from Near Sources in Method of Moments Layered Media Solvers","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Method of moments (probability theory); Discretization; Integral equation; Dipole; Kernel (algebra); Matrix (chemical analysis); Dielectric; Computer science; Maxwell's equations; Mathematical analysis; Physics; Materials science; Mathematics; Optoelectronics; Quantum mechanics; Composite material","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":[],"consensus_categories":[],"category_scores_codex":[0.001268744,0.0001434498,0.0002410916,0.0001842719,0.0001437204,0.0001185865,0.0001928629,0.00004335447,0.0001818822],"category_scores_gemma":[0.0000950131,0.0001325892,0.00007162462,0.000368925,0.000177098,0.0001437409,0.00004193733,0.0001351519,0.000001940147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007446941,"about_ca_system_score_gemma":0.000161285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007336036,"about_ca_topic_score_gemma":0.00004085481,"domain_scores_codex":[0.9979672,0.0001638735,0.0003829503,0.0004321828,0.0008644777,0.0001892572],"domain_scores_gemma":[0.9989004,0.0002093801,0.0002797948,0.0001542692,0.0003736861,0.00008250665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002122079,0.0005854763,0.07714853,0.00003905977,0.0002566524,0.000008706495,0.00710947,0.02304788,0.8004624,0.005683571,0.0001471544,0.08529893],"study_design_scores_gemma":[0.002311693,0.0003270545,0.0465414,0.0008429937,0.0001750634,0.000006033878,0.00367562,0.4972122,0.4459536,0.002506702,0.00006368016,0.000383986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937586,0.00006917275,0.00202479,0.001158888,0.0003607587,0.0001061025,0.00002735044,0.00000599437,0.002488324],"genre_scores_gemma":[0.9984276,0.00003675078,0.001218274,0.00005674983,0.0001066758,0.000009599607,0.00004098114,0.000007051397,0.00009628241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4741643,"threshold_uncertainty_score":0.5406832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01689552105800844,"score_gpt":0.2964426784591039,"score_spread":0.2795471574010954,"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."}}