{"id":"W3003663007","doi":"","title":"Recent Advances in EM Parametric Modeling Using Combined Neural Network and Transfer Function","year":2019,"lang":"en","type":"article","venue":"International Symposium on Antennas and Propagation","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Transfer function; Artificial neural network; Computer science; Parametric statistics; Parametric model; Antenna (radio); Wideband; Computational electromagnetics; Data modeling; Function (biology); Artificial intelligence; Electronic engineering; Engineering; Telecommunications; Physics; Electromagnetic field; Mathematics","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":[0.0007625656,0.0007044184,0.0005864617,0.0006829011,0.0001564778,0.0008182489,0.0009413026,0.0008321896,0.00144604],"category_scores_gemma":[0.00138482,0.0003458869,0.0006750973,0.0008618286,0.0003470941,0.001353744,0.0005148211,0.0007793289,0.0005791893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003687033,"about_ca_system_score_gemma":0.0003434144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001831874,"about_ca_topic_score_gemma":0.001348246,"domain_scores_codex":[0.9997144,0.00008394187,0.00002235818,0.00004687032,0.0001154788,0.00001681634],"domain_scores_gemma":[0.9994773,0.0002633166,0.00004741211,0.00005386008,0.0001458959,0.00001219017],"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":[0.00007069411,0.00005322686,0.0008559852,0.0003548053,0.0001005902,0.0001105623,0.00005445367,0.6976032,0.005383628,0.01095624,0.001020324,0.2834364],"study_design_scores_gemma":[0.000002019521,0.00001997183,0.0001807452,0.00002185836,0.00001341415,0.00003655696,0.000007017389,0.9912528,0.001345954,0.002715339,0.004394813,0.000009486219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0109259,0.010641,0.9706787,0.0003881259,0.00009291102,0.00001761188,0.00004164763,0.000435132,0.006778975],"genre_scores_gemma":[0.5635644,0.04199443,0.3788841,0.000334057,0.0006652355,0.0001488063,0.0003189674,0.000299969,0.01378992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001831874,"threshold_uncertainty_score":0.004837453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163154096538498,"score_gpt":0.2173523535339802,"score_spread":0.2057208125685952,"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."}}