{"id":"W2066816199","doi":"10.1049/ip-map:20000801","title":"Investigation of projection iterative method in solving MoM matrix equations in electromagnetic scattering","year":2000,"lang":"en","type":"article","venue":"IEE Proceedings - Microwaves Antennas and Propagation","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":"Invertible matrix; Iterative method; Matrix (chemical analysis); Mathematics; Relaxation (psychology); Rate of convergence; Convergence (economics); Mathematical analysis; Scattering; Applied mathematics; Projection (relational algebra); Residual; Cylinder; Computational electromagnetics; Electromagnetic field; Mathematical optimization; Algorithm; Computer science; Physics; Geometry; Optics; Materials science","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":[0.001726678,0.000580736,0.0005168188,0.0003038308,0.0003385835,0.0005527991,0.0006573616,0.0007615366,0.001373703],"category_scores_gemma":[0.004143891,0.000301478,0.0004869769,0.0006326616,0.0005497513,0.0009672043,0.0008110777,0.001011459,0.0004639814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224793,"about_ca_system_score_gemma":0.0007058067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009924765,"about_ca_topic_score_gemma":0.0006660809,"domain_scores_codex":[0.9990712,0.0004581596,0.00002424064,0.00005058962,0.0003628086,0.00003286224],"domain_scores_gemma":[0.9986023,0.0009103143,0.00005304143,0.0001005314,0.0003020963,0.00003155911],"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":[0.0002593875,0.0001414192,0.0035394,0.0009003012,0.0001241167,0.000619447,0.0007329466,0.5395558,0.03291408,0.1493269,0.002835769,0.2690504],"study_design_scores_gemma":[0.00000850871,0.00006864908,0.0001470306,0.00002026078,0.000007173161,0.0001347191,0.00002423416,0.9877554,0.004676807,0.00489619,0.002254085,0.000007115989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007493649,0.0004122942,0.9880908,0.00008091917,0.00002612761,0.00003979095,0.000008784215,0.0001445174,0.003703068],"genre_scores_gemma":[0.1771163,0.001654763,0.8174779,0.00006929049,0.00006110807,0.0002400614,0.00007182199,0.0001332321,0.003175534],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001726678,"threshold_uncertainty_score":0.00913161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009875415479972916,"score_gpt":0.2530768303933612,"score_spread":0.2432014149133882,"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."}}