{"id":"W2345022821","doi":"10.1109/tap.2016.2560958","title":"On New Triangle Quadrature Rules for the Locally Corrected Nyström Method Formulated on NURBS-Generated Bézier Surfaces in 3-D","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Georgia Institute of Technology","keywords":"Quadrilateral; Gaussian quadrature; Quadrature (astronomy); Numerical integration; Mathematics; Gauss–Kronrod quadrature formula; Clenshaw–Curtis quadrature; Tanh-sinh quadrature; Gauss–Laguerre quadrature; Adaptive quadrature; Bézier curve; Mathematical analysis; Applied mathematics; Algorithm; Geometry; Computer science; Nyström method; Integral equation; Finite element method","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":[],"consensus_categories":[],"category_scores_codex":[0.0001747328,0.0001938976,0.0002236094,0.0001613966,0.0001175843,0.00003382397,0.0000783887,0.0001337832,0.0000392804],"category_scores_gemma":[0.00002794089,0.00010862,0.00008044459,0.0003800656,0.00002732521,0.0001396933,4.692207e-7,0.0001922673,0.000008221458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007757784,"about_ca_system_score_gemma":0.00001699221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004058049,"about_ca_topic_score_gemma":0.0001631639,"domain_scores_codex":[0.9991286,0.00006473204,0.0002612257,0.0002300848,0.0001270505,0.0001883181],"domain_scores_gemma":[0.9991943,0.0004635077,0.00004996308,0.0001649847,0.00007016934,0.00005712956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009003617,0.0001172192,0.000003488618,0.00002653397,0.0001111926,0.000001596272,0.000152497,0.1389364,0.2189533,0.000319045,0.0004844528,0.6399938],"study_design_scores_gemma":[0.001465714,0.0005420229,0.0001990726,0.0002382384,0.00006501954,0.000002684182,0.00003861134,0.5071688,0.4869696,0.002008247,0.001005961,0.000295961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02421647,0.00008324968,0.9739466,0.0006539756,0.0001685736,0.0005677009,0.00004105357,0.0002767609,0.0000456227],"genre_scores_gemma":[0.9923332,0.0002599303,0.006505116,0.0001236871,0.00002776796,0.00009511987,0.00001155156,0.00003509434,0.0006085082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9681168,"threshold_uncertainty_score":0.4429397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436525389559251,"score_gpt":0.261390517430746,"score_spread":0.2470252635351535,"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."}}