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Record W2008943563 · doi:10.1109/mmm.2008.930757

A technique for differential noise figure measurement with a noise figure analyzer [Technical Committee]

2009· article· en· W2008943563 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Microwave Magazine · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Electrical Measurement Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNoise figureBalunNoise (video)Low-noise amplifierNoise temperatureEffective input noise temperatureNoise measurementNoise-figure meterElectronic engineeringSpectrum analyzerDifferential (mechanical device)Computer scienceElectrical engineeringNoise generatorY-factorAmplifierEngineeringNoise reductionPhase noiseArtificial intelligence

Abstract

fetched live from OpenAlex

This article summarizes a new approach presented in [1] to measure the noise figure of a differential low-noise amplifier (LNA) based on familiar cold-hot single-ended noise figure measurements employed by the majority of noise figure analyzers/meters. This approach does not require external baluns or hybrids that have traditionally been employed in noise figure measurements of differential circuits. We envision that this technique could be automated in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.237
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it