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Record W2755403189 · doi:10.1364/optica.4.001143

Reproducible ultra-long FBGs in phase corrected non-uniform fibers

2017· article· en· W2755403189 on OpenAlex
Sébastien Loranger, Victor Lambin-Iezzi, Raman Kashyap

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptica · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFabricationFiber Bragg gratingMaterials scienceOpticsOptical fiberPhase (matter)FiberPHOSFOSOptoelectronicsPlastic optical fiberFiber optic sensorComposite materialPhysics

Abstract

fetched live from OpenAlex

Ultra-long fiber Bragg gratings (FBGs), i.e., FBGs of several tens of cm in length, have attracted much attention in the last few decades for their potential applications in advanced devices. Although numerous fabrication methods as well as ultra-long functionalized FBGs have been proposed and demonstrated successfully, such devices are difficult to reproduce. We have recently found that specialty optical fibers of the type required for these applications are highly non-uniform on a short length scale, severely affecting the characteristics of ultra-long FBGs. We propose here a new production technique that can be adapted to any non-uniform fiber for ultra-long FBG fabrication. This technique involves a fiber characterization prior to FBG inscription followed by the writing of a phase corrected ultra-long FBG. This technique has no limitations in terms of correction amplitude or FBG length. The results are quite astonishing, as near-perfect 1-m-length-scale FBGs are possible in fibers in which it was impossible to write uniform period gratings prior to phase correction.

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.

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 score0.187
Threshold uncertainty score0.849

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 head0.015
GPT teacher head0.273
Teacher spread0.259 · 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