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Record W2942723650 · doi:10.1364/oe.27.013895

The ROGUE: a novel, noise-generated random grating

2019· article· en· W2942723650 on OpenAlexafffund
Frédéric Monet, Sébastien Loranger, Victor Lambin-Iezzi, Antoine Drouin, Samuel Kadoury, Raman Kashyap

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsReflectometryMaterials scienceFiber Bragg gratingGratingBackscatter (email)Optical fiberRayleigh scatteringOptoelectronicsPhysicsTime domainTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We propose a novel device defined as Random Optical Grating by Ultraviolet or ultrafast laser Exposure (ROGUE), a new type of fiber Bragg grating (FBG), exhibiting a weak reflection over a large bandwidth, which is independent of the length of the grating. This FBG is fabricated simply by dithering the phase randomly during the writing process. This grating has an enhanced backscatter, several orders of magnitude above typical Rayleigh backscatter of standard SMF-28 optical fiber. The grating is used in distributed sensing using optical frequency domain reflectometry (OFDR), allowing a significant increase in signal to noise ratio for strain and temperature measurement. This enhancement results in significantly lower strain or temperature noise level and accuracy error, without sacrificing the spatial resolution. Using this method, we show a sensor with a backscatter level 50 dB higher than standard unexposed SMF-28, which can thus compensate for increased loss in the system.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.570

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 designSimulation or modeling
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
Published2019
Admission routes2
Has abstractyes

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