{"id":"W3162603385","doi":"10.23919/eucap51087.2021.9411270","title":"Fast Modeling of Electromagnetic Scattering from Dielectrics or Conductors with an Extended Adaptive Integral Method","year":2021,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dielectric; Scattering; Integral equation; Computer science; Electrical conductor; Point (geometry); Electronic engineering; Computational electromagnetics; Surface (topology); Numerical integration; Computational science; Electromagnetics; Optics; Physics; Electromagnetic field; Mathematics; Mathematical analysis; Engineering; Electrical engineering; Optoelectronics; Geometry","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":[0.0001002017,0.0002509173,0.0004416095,0.0001009617,0.00008177803,0.00006152123,0.0001957275,0.00004069726,0.001628897],"category_scores_gemma":[0.000006648062,0.0001850023,0.0001210433,0.0005686304,0.00003818234,0.0001377902,0.00004633074,0.0002254472,0.000003752195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002321714,"about_ca_system_score_gemma":0.0001755018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002534513,"about_ca_topic_score_gemma":0.0001991604,"domain_scores_codex":[0.9984499,0.0001273308,0.000324033,0.0004930401,0.000214189,0.0003915201],"domain_scores_gemma":[0.999097,0.00009998844,0.0001066647,0.0003776403,0.0001990992,0.0001196153],"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":[0.0004421226,0.0007163556,0.003347612,0.00002119693,0.001196905,0.00003209242,0.001234673,0.007819274,0.8727379,0.005407147,0.00003735575,0.1070074],"study_design_scores_gemma":[0.001113085,0.00218968,0.0005626913,0.00008217381,0.0005171213,0.00001386256,0.004890758,0.6801856,0.3039702,0.005827112,0.0000092972,0.0006383518],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6922253,0.00005818717,0.3055681,0.00005768249,0.00001753772,0.00005294436,0.00001283898,0.00003269898,0.001974703],"genre_scores_gemma":[0.8761905,0.000003737556,0.1227599,0.00004011737,0.00009820701,0.00001423064,0.00006640271,0.00002952748,0.0007973884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6723663,"threshold_uncertainty_score":0.9992837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02041626056222371,"score_gpt":0.2703684659439957,"score_spread":0.249952205381772,"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."}}