{"id":"W1895327854","doi":"10.1002/nbm.3138","title":"A system for automated noise parameter measurements on MR preamplifiers and application to high <i>B</i><sub>0</sub> fields","year":2014,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Preamplifier; Noise figure; Amplifier; Noise (video); Noise temperature; Low-noise amplifier; Flicker noise; Y-factor; Electrical engineering; Noise generator; Effective input noise temperature; Noise-figure meter; Noise spectral density; Noise measurement; Physics; Spectrum analyzer; Optoelectronics; Materials science; Computer science; Acoustics; Engineering; CMOS; Noise reduction; Phase noise","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":[0.0004316925,0.0002016787,0.0002807748,0.0003001649,0.00003196201,0.0000191176,0.0001383872,0.0001710551,0.000001249616],"category_scores_gemma":[0.0001184349,0.0001785367,0.00002446609,0.0003844106,0.00003278425,0.00004744972,0.000002362855,0.0001225004,0.00002416392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002540066,"about_ca_system_score_gemma":0.00001069902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006216649,"about_ca_topic_score_gemma":0.00001674964,"domain_scores_codex":[0.9988156,0.00003448978,0.0003350841,0.0002958208,0.000225508,0.0002934819],"domain_scores_gemma":[0.9992957,0.0001468333,0.00003907348,0.0003193823,0.00005944732,0.0001396229],"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":[0.00004454252,0.000030999,0.0003849028,0.0003886399,0.00005310249,0.000002062751,0.000218991,0.01118754,0.9537821,0.0003374776,0.005453149,0.02811648],"study_design_scores_gemma":[0.002657718,0.0007620928,0.003709813,0.0008620068,0.00007168602,0.00001740156,0.00007705093,0.2344614,0.7550092,0.0003756881,0.001502112,0.0004938815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7539694,0.0001157639,0.2396183,0.0003516682,0.0006753338,0.001989438,0.00002962738,0.001283573,0.001966941],"genre_scores_gemma":[0.9985075,0.000007328742,0.0004628916,0.0002645409,0.000116821,0.0005602085,0.00003520962,0.00004098233,0.000004546238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2445381,"threshold_uncertainty_score":0.7280514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476053172549395,"score_gpt":0.2355343055225245,"score_spread":0.2207737737970306,"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."}}