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Record W2997226759 · doi:10.1109/lmwc.2019.2952977

Microwave Characterization of Liquid Samples Through the Systematic Parameter Extraction of the Circuit Equivalence for the Debye Model

2019· article· en· W2997226759 on OpenAlexaff
Eduardo Moctezuma-Pascual, Gabriela Méndez‐Jerónimo, Zail Omán Rodríguez-Moré, Humberto Lobato‐Morales, Reydezel Torres‐Torres

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsPolytechnique Montréal
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsDebyePermittivityEquivalence (formal languages)MicrowaveEquivalent circuitExtraction (chemistry)Scattering parametersElectronic engineeringCharacterization (materials science)Materials scienceComputational physicsPhysicsMathematicsEngineeringCondensed matter physicsElectrical engineeringOptoelectronicsOpticsDielectricChemistryQuantum mechanicsVoltageChromatography

Abstract

fetched live from OpenAlex

This letter presents a parameter extraction methodology for implementing the Debye model to represent the complex permittivity of liquids at microwave frequencies. Thus, based on an electrical circuit analogy, the parameters associated with different model's poles as well as with the low-frequency loss are straightforwardly determined through linear regressions. Excellent model-experiment agreement is achieved up to 15 GHz for four different liquid samples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.230
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2019
Admission routes1
Has abstractyes

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