{"id":"W4383376470","doi":"10.1029/2022rs007623","title":"A Novel Low‐Loss Planar PRGW Crossover Design for 5G Applications","year":2023,"lang":"en","type":"article","venue":"Radio Science","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Institut national de la recherche scientifique","keywords":"Crossover; Planar; Bandwidth (computing); Return loss; Insertion loss; Extremely high frequency; Millimeter; Computer science; Optics; Materials science; Telecommunications; Physics","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":[0.0001678956,0.0003225595,0.000257649,0.0003226674,0.0001707987,0.0005737443,0.0006726137,0.0006445951,0.0009391977],"category_scores_gemma":[0.0002138941,0.0001760549,0.0003772002,0.0003351766,0.000227781,0.0004946322,0.0002356963,0.0003610598,0.0004307572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002520262,"about_ca_system_score_gemma":0.0001792724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001276635,"about_ca_topic_score_gemma":0.0001901559,"domain_scores_codex":[0.9998432,0.00001943289,0.000006230465,0.00005205701,0.00004820349,0.00003091482],"domain_scores_gemma":[0.9997188,0.0000325084,0.0001313047,0.00003992326,0.00005245387,0.00002491926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001389977,0.00005906263,0.0006880742,0.0001397689,0.00004071834,0.0003120402,0.00007100681,0.002422169,0.971982,0.00406512,0.0007262368,0.01935476],"study_design_scores_gemma":[0.0001693258,0.002799538,0.006250446,0.00005698433,0.0001351431,0.004179164,0.0001511431,0.04186699,0.900766,0.001841078,0.04167936,0.0001048032],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7697492,0.001671565,0.2130629,0.0004941788,0.0002317557,0.0001161408,0.0002552549,0.001378193,0.01304069],"genre_scores_gemma":[0.9453464,0.0003050477,0.05180471,0.00007079917,0.00003972978,0.00004064806,0.0001548133,0.00003572314,0.002202029],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009391977,"threshold_uncertainty_score":0.00314188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02279938704206916,"score_gpt":0.2470792124916162,"score_spread":0.2242798254495471,"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."}}