{"id":"W4394595001","doi":"10.1109/lmwt.2024.3383335","title":"Millimeter-Wave Device Characterization Under Wideband Modulated Signals Using Vector Network Analyzer Frequency Extenders","year":2024,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Technology Letters","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wideband; Spectrum analyzer; Extremely high frequency; Characterization (materials science); Network analyzer (electrical); Millimeter; Materials science; Computer science; Electronic engineering; Optics; Physics; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001484176,0.0004660304,0.0004472531,0.0006782935,0.000136902,0.0001579489,0.0002040881,0.0005256147,0.00002219471],"category_scores_gemma":[0.000005231138,0.0004757439,0.0001005017,0.001371676,0.0002699373,0.0003344907,0.0000072252,0.0006246572,0.00002441221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002683236,"about_ca_system_score_gemma":0.00003741863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003387915,"about_ca_topic_score_gemma":0.00001390859,"domain_scores_codex":[0.9980287,0.00005871132,0.000479214,0.0005924591,0.0001444914,0.0006964062],"domain_scores_gemma":[0.9993618,0.00006384528,0.00006365422,0.0003663604,0.0000563383,0.00008806457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003212973,0.000007953278,0.0001755955,0.00009493696,0.000442985,0.0002618089,0.00009269318,0.006922994,0.9857444,0.000272903,0.0004659135,0.005514558],"study_design_scores_gemma":[0.0002252411,0.00003563539,0.0003328572,0.0004459219,0.0002003793,0.0003927073,0.00004540183,0.07129545,0.9243718,0.001797619,0.0001474296,0.0007096077],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7938739,0.00222148,0.2009079,0.0007015025,0.0008385535,0.0002519831,0.00002207254,0.001113601,0.00006905236],"genre_scores_gemma":[0.9978931,0.0003396553,0.0006520503,0.0006878138,0.0001943152,0.00003040544,0.00005055385,0.0001395077,0.00001262479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2040192,"threshold_uncertainty_score":0.9997694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993124807837078,"score_gpt":0.2194538770677275,"score_spread":0.1995226289893567,"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."}}