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Record W2142376090 · doi:10.1142/s0219477508004313

WAVE-BASED APPROACH FOR MICROWAVE NOISE CHARACTERIZATION

2008· article· en· W2142376090 on OpenAlexaff
Caiwen Chen, Ying-Lien Wang, Mohamed H. Bakr

Bibliographic record

VenueFluctuation and Noise Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNoise (video)Noise measurementAcousticsComputer scienceNoise temperaturePort (circuit theory)MicrowavePhysicsElectronic engineeringTelecommunicationsNoise reductionEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The noise behavior of a two-port is usually described through the conventional set of noise parameters F min , R n , and the complex Y opt . However, noise parameters developed using wave-based techniques also have their merit as they could offer different insights to a two-port's noise behavior. Unlike the conventional noise parameters, these wave-based noise parameters could be terminal-invariant and describe only the intrinsic noise behavior of a two-port. In this paper, several important noise parameters derived from wave-based approaches are reviewed. The derivation of each set of parameters is discussed and illustrated. The measurement approach of each set of parameters is also briefly covered.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.191
Teacher spread0.168 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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