{"id":"W2982557306","doi":"10.1109/apusncursinrsm.2019.8888868","title":"Fast and Accurate Near-Field to Far-Field Transformation Using an Adaptive Sampling Algorithm and Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Near and far field; Field (mathematics); Transformation (genetics); Algorithm; Antenna (radio); Computer science; Sampling (signal processing); Adaptive sampling; Projection (relational algebra); Mathematics; Telecommunications; Optics; Physics; Monte Carlo method; Detector","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":[0.0001584873,0.0001175962,0.0001302943,0.00004740827,0.00008409227,0.00007750438,0.00004245674,0.00005406651,0.00007451468],"category_scores_gemma":[0.00001593158,0.0001177946,0.00001407126,0.00008503216,0.000005472671,0.0002659922,0.00001525837,0.0001849891,0.000004650316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002110061,"about_ca_system_score_gemma":0.000008920426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003176212,"about_ca_topic_score_gemma":0.0001901776,"domain_scores_codex":[0.9993793,0.00002135204,0.0001500776,0.0001543412,0.0001054468,0.0001895094],"domain_scores_gemma":[0.9997212,0.00005592567,0.00001308946,0.00008756039,0.00002755734,0.00009462934],"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":[0.00005715997,0.00002309368,0.00178429,0.00008864216,0.00003616292,7.621104e-7,0.003795659,0.07312068,0.0668069,0.0001154498,0.000004164032,0.854167],"study_design_scores_gemma":[0.0003113858,0.0007781044,0.002056142,0.0000340618,0.00001273692,0.000006647538,0.0003463107,0.9858129,0.01017062,0.00004630099,0.000255191,0.0001695678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6242206,0.00009117828,0.3745654,0.00003245399,0.00004469947,0.0001734322,0.000001196562,0.00007267406,0.0007984444],"genre_scores_gemma":[0.9742148,0.0000286656,0.02557826,0.0001123567,0.00001592485,0.00000294825,0.000003565139,0.00001295057,0.00003053401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9126922,"threshold_uncertainty_score":0.4803525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03788745538532018,"score_gpt":0.2518871586069569,"score_spread":0.2139997032216367,"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."}}