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Record W2068474861 · doi:10.1088/0957-0233/17/7/013

Ridge-waveguide-based polarization insensitive Bragg grating refractometer

2006· article· en· W2068474861 on OpenAlexaff
Xiaoli Dai, Stephen J. Mihailov, Claire L. Callender, Chantal Blanchetière, Robert B. Walker

Bibliographic record

VenueMeasurement Science and Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsRefractometerFiber Bragg gratingOpticsMaterials scienceGratingWaveguidePolarization (electrochemistry)Refractive indexWavelengthUltrasonic gratingOptoelectronicsBlazed gratingDiffraction gratingPhysicsChemistry

Abstract

fetched live from OpenAlex

A highly sensitive waveguide Bragg grating (WBG) sensor for measuring small changes of the refractive index of the surrounding liquid is presented. By using an open top ridge waveguide with a small core, the evanescent field interaction of the guided mode with the liquid analyte on the top of the waveguide is enhanced. The sensitivity measured via a shift in the resonance wavelength of the Bragg grating as high as 1 pm of wavelength shift for a change of 4 × 10 −5 in the refractive index around 1.402 is realized. With a polarization insensitive Bragg grating, the polarization dependence of the sensor is improved. A theoretical analysis for the sensitivity of ridge waveguide sensors is given. The experimental results are in good agreement with the theoretical analysis.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.208
Teacher spread0.194 · 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

Citations17
Published2006
Admission routes1
Has abstractyes

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