{"id":"W2046949301","doi":"10.1109/usnc-ursi.2013.6715449","title":"Efficient frequency domain technique for electromagnetic scattering from arbitrary objects using the Random Auxiliary Sources","year":2013,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Method of moments (probability theory); Integral equation; Solver; Moment (physics); Computer science; Iterative method; Boundary value problem; Boundary (topology); Mathematical analysis; Algorithm; Applied mathematics; Mathematical optimization; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001802738,0.0002710606,0.0003095625,0.00008909274,0.0003575598,0.0001763026,0.0003470107,0.00005131664,0.001371545],"category_scores_gemma":[0.000005400958,0.0001871202,0.0002773902,0.0002407218,0.00009614923,0.00005413977,0.00005840661,0.0001925355,0.00002917353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002803826,"about_ca_system_score_gemma":0.00005735851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00554682,"about_ca_topic_score_gemma":0.00001386334,"domain_scores_codex":[0.9984416,0.00009319563,0.0003419859,0.0004069346,0.0001713118,0.0005449964],"domain_scores_gemma":[0.9990497,0.0002649382,0.0001183775,0.0004209709,0.00005701914,0.00008903427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001073638,0.00007297059,0.003265779,0.00000934591,0.0001286765,4.932891e-7,0.0002357027,0.0002064652,0.9942987,0.0007022989,0.0001551191,0.0009136865],"study_design_scores_gemma":[0.004990253,0.000693695,0.005516428,0.0002599883,0.0007844607,0.00001827708,0.004875655,0.07242946,0.6029217,0.3054024,0.0001273988,0.001980327],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8609168,0.0001907596,0.134884,0.0004428852,0.00004700479,0.0008322589,0.0000115912,0.00006523734,0.002609447],"genre_scores_gemma":[0.9485623,0.000001006313,0.0500525,0.0001928833,0.0003527408,0.0005933643,0.0000248532,0.00003624562,0.0001841053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.391377,"threshold_uncertainty_score":0.9995413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006211748586754469,"score_gpt":0.2163138777895618,"score_spread":0.2101021292028073,"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."}}