{"id":"W1544424269","doi":"10.1109/aps.1998.690836","title":"Performances of projection iterative method on electromagnetic scattering by spheres","year":2002,"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":"Iterative method; Computational electromagnetics; Electromagnetics; Scattering-matrix method; Projection (relational algebra); Convergence (economics); Applied mathematics; Computer science; Method of moments (probability theory); Decomposition method (queueing theory); Matrix (chemical analysis); Projection method; Integral equation; Scattering; Mathematical optimization; Mathematics; Algorithm; Mathematical analysis; Maxwell's equations; Electromagnetic field; Dykstra's projection algorithm; Physics; Optics","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":[0.0009859467,0.0006654686,0.0007666733,0.0004104899,0.0004541001,0.0006528445,0.0006313668,0.0005895108,0.00296791],"category_scores_gemma":[0.003693564,0.0002388421,0.0004924771,0.0007469399,0.0004769446,0.0008852965,0.00110794,0.000641875,0.001002744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002567956,"about_ca_system_score_gemma":0.0009892343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005786753,"about_ca_topic_score_gemma":0.001888843,"domain_scores_codex":[0.9992125,0.0002850218,0.00002653162,0.00006236337,0.0003438799,0.00006957846],"domain_scores_gemma":[0.9982238,0.000886607,0.00006483756,0.0001721955,0.0005814477,0.00007110921],"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":[0.000843155,0.00009764099,0.002212134,0.0002410163,0.00009073046,0.000251357,0.0005198447,0.7408378,0.02019317,0.02617431,0.003086951,0.205452],"study_design_scores_gemma":[0.000008974058,0.00003084952,0.0001440562,0.000004658554,0.000004036009,0.00003276223,0.00001673956,0.9937971,0.003646055,0.001816279,0.0004908158,0.000007818875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1128181,0.0005609882,0.8683352,0.0001797766,0.00007388098,0.00007009802,0.0001097555,0.001684109,0.01616807],"genre_scores_gemma":[0.6208898,0.0006369483,0.3721321,0.00004722543,0.00002908581,0.0001178233,0.0003765095,0.0003776811,0.005392763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005786753,"threshold_uncertainty_score":0.01150614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008339731568975719,"score_gpt":0.2433688645216454,"score_spread":0.2350291329526697,"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."}}