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Record W2706017363 · doi:10.1109/lmwc.2017.2711527

Miniaturized Quarter-Mode Substrate Integrated Cavity Resonators for Humidity Sensing

2017· article· en· W2706017363 on OpenAlexafffundabout
Thomas R. Jones, Mohammad H. Zarifi, Mojgan Daneshmand

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates - Technology FuturesCMC Microsystems
KeywordsResonatorMiniaturizationQuarter (Canadian coin)Sensitivity (control systems)Materials scienceHumidityRelative humidityQ factorSubstrate (aquarium)Mode (computer interface)OptoelectronicsElectrical engineeringOpticsElectronic engineeringEngineeringPhysicsComputer scienceNanotechnologyMeteorology

Abstract

fetched live from OpenAlex

This letter presents the substantial miniaturization of substrate integrated waveguide sensors using quarter-mode and ridged quarter-mode techniques, for detecting changes in relative humidity levels between 0%-80%. Miniaturizations of 73.3% and 86.2% are reported with resonant frequencies of 6 GHz and 6.9 GHz for the two techniques, respectively. Furthermore, an increase in humidity sensitivity is achieved almost four times greater than previously reported. Due to the reduced size and increased sensitivity, these two techniques open up new possibilities and applications for sensor design. To the best of the authors' knowledge, this letter also reports the first ridged quarter-mode resonant structure, providing even greater miniaturizations and improved quality factor of quarter-mode structures in the literature.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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

Citations49
Published2017
Admission routes3
Has abstractyes

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