{"id":"W4224434710","doi":"10.21203/rs.3.rs-1581447/v1","title":"Analysis and Design of Ultra-Wideband PRGW Hybrid Coupler Using PEC/PMC Waveguide Model","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Hybrid coupler; Return loss; Power dividers and directional couplers; Wideband; Bandwidth (computing); Microwave; Rat-race coupler; Extremely high frequency; Magic tee; Electronic engineering; Insertion loss; Acoustics; Computer science; Engineering; Electrical engineering; Telecommunications; Physics; Antenna (radio)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002555774,0.0005652813,0.000446908,0.0003204391,0.0001990772,0.0006726342,0.000717634,0.0007346439,0.002118084],"category_scores_gemma":[0.0002066675,0.0004435995,0.0008475371,0.0002656998,0.000267059,0.0005679035,0.0002558581,0.0003013349,0.0007634247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003702276,"about_ca_system_score_gemma":0.0003159182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007077793,"about_ca_topic_score_gemma":0.0007024937,"domain_scores_codex":[0.9997846,0.00003384038,0.000006411701,0.00003908344,0.000115779,0.00002031856],"domain_scores_gemma":[0.9999046,0.00001987777,0.00002637251,0.00001365448,0.00002952573,0.000005961948],"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":[0.0001642805,0.000120387,0.0016781,0.0004868213,0.0001632424,0.0009953787,0.0001864326,0.5074469,0.4080178,0.04611892,0.001926569,0.03269511],"study_design_scores_gemma":[0.00001797108,0.000131935,0.0003681573,0.00001546218,0.00002160437,0.0002465174,0.00002749462,0.9764122,0.01818803,0.0008402472,0.003714952,0.00001546122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09731623,0.0009163245,0.8758683,0.0002348845,0.00007431128,0.0001158844,0.0001189254,0.000796046,0.02455907],"genre_scores_gemma":[0.8085238,0.0009974641,0.177753,0.00006960848,0.00003421252,0.0002414393,0.000135847,0.0001291491,0.01211555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002118084,"threshold_uncertainty_score":0.007085681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06973065874736717,"score_gpt":0.3300231615541592,"score_spread":0.260292502806792,"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."}}