{"id":"W2952002158","doi":"10.1109/tap.2019.2923074","title":"Dual-Band Microstrip Corporate Feed Network Using an Embedded Metamaterial-Based EBG","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Metamaterial; Metamaterial antenna; Multi-band device; Materials science; Microstrip; Bandwidth (computing); Transformer; Optoelectronics; Power dividers and directional couplers; Electrical length; Microstrip antenna; Return loss; Insertion loss; Wilkinson power divider; Computer science; Electrical engineering; Optics; Telecommunications; Physics; Voltage; Antenna (radio); Engineering; Slot antenna","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001885517,0.0002196958,0.0002195016,0.0001168994,0.0001208962,0.0001129169,0.00004717245,0.0001107356,0.00007161812],"category_scores_gemma":[0.000001230475,0.0002125429,0.00005381381,0.0002027728,0.00002890425,0.0001965299,3.962001e-7,0.0001530107,0.0000216865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003881969,"about_ca_system_score_gemma":0.00002190894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001839989,"about_ca_topic_score_gemma":0.00001219892,"domain_scores_codex":[0.9991241,0.00004108999,0.0002500787,0.0002297369,0.0001019224,0.0002530916],"domain_scores_gemma":[0.9995764,0.00002249956,0.0000562685,0.0002020492,0.00005346682,0.00008934143],"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":[0.0000381949,0.0000208405,0.00001765921,0.0000678819,0.00002627924,0.000002110035,0.00005790955,0.4321701,0.5668442,0.000009262904,0.00001794216,0.0007276125],"study_design_scores_gemma":[0.0005671768,0.0001094915,0.0002035783,0.00008246123,0.00004274294,0.00001677641,0.00002414879,0.5926803,0.4058806,0.00003236973,0.0001123216,0.0002480039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6037262,0.00005699707,0.394976,0.00001311729,0.0007679383,0.0002047914,0.00002252784,0.0001939596,0.00003847371],"genre_scores_gemma":[0.9978161,0.00003141075,0.001793028,0.00004261082,0.00008854303,0.00001076208,0.00002536923,0.00005276654,0.0001393556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3940899,"threshold_uncertainty_score":0.8667247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243049882659829,"score_gpt":0.2146999915423149,"score_spread":0.1922694927157167,"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."}}