{"id":"W2903442432","doi":"10.23919/eumc.2018.8541722","title":"Accurate Millimeter-wave Carrier Frequency Offset Measurement Using the Six-port Interferometric Technique","year":2018,"lang":"en","type":"article","venue":"","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Interferometry; Offset (computer science); Amplitude; Local oscillator; Extremely high frequency; Carrier frequency offset; Frequency offset; Millimeter; Calibration; Doppler effect; Physics; Optics; Radio frequency; Frequency band; Frequency synthesizer; Phase noise; Bandwidth (computing); Computer science; Orthogonal frequency-division multiplexing; Telecommunications; Channel (broadcasting); Phase-locked loop","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001189552,0.0003073186,0.0002520826,0.0004075367,0.0002476374,0.00006504848,0.000325136,0.0001485696,0.0004966613],"category_scores_gemma":[0.0001409201,0.0002148303,0.0001135867,0.001104466,0.00007953154,0.0001695379,0.0000865716,0.0002677232,0.0000243341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003509646,"about_ca_system_score_gemma":0.00006284771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001243703,"about_ca_topic_score_gemma":0.0001029775,"domain_scores_codex":[0.9981632,0.00006538249,0.0004687872,0.0002855774,0.0005407166,0.0004763888],"domain_scores_gemma":[0.9988164,0.00002634949,0.0000689051,0.0005886769,0.0004010055,0.00009871056],"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":[0.000006561096,0.00002752003,0.0002524602,0.00003842243,0.0001304055,0.000007389955,0.0001752225,0.000007749402,0.9822195,0.0001643084,0.01474547,0.002224961],"study_design_scores_gemma":[0.0001056163,0.000169548,0.0001338778,0.00007416672,0.00005688009,0.00004740859,0.00003341835,0.003791231,0.9898984,0.0004714947,0.004857879,0.0003600773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05328971,0.001018631,0.9079365,0.00005651993,0.0004539217,0.001115824,0.00001346565,0.00117077,0.03494462],"genre_scores_gemma":[0.987668,0.00007758642,0.01166621,0.0001864737,0.0001951907,0.00008941738,0.000003760246,0.00005330565,0.00006000311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9343783,"threshold_uncertainty_score":0.8760527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08324154893660282,"score_gpt":0.2602632488995129,"score_spread":0.17702169996291,"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."}}