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Record W4399666185 · doi:10.1117/12.3022568

Fibre Bragg gratings in few-mode and multimode fibres

2024· article· en· W4399666185 on OpenAlexaff
Polina Zavyalova, Shravan Kruthick Sridhar, Suresh Sivanandam, Peter R. Herman, Boyan Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMulti-mode optical fiberPHOSFOSMaterials scienceFiber Bragg gratingOpticsMode (computer interface)OptoelectronicsOptical fiberComputer sciencePlastic optical fiberPhysics

Abstract

fetched live from OpenAlex

Fibre Bragg gratings (FBGs) have found several uses in astronomical instrumentation such as spectral filtering and spectroscopic analysis. FBGs are conventionally inscribed in single-mode fibres where spectral responses are easily modelled and desired features are readily fabricated. Gratings in multimode fibres yield multiple Bragg resonances that can pose restrictions in their applications. However, direct writing of FBGs selectively across the cross-section of the multimode core waveguide has the potential to tailor the grating coupling independently for each mode and enable modeselective responses for a more versatile spectral and beam control filtering by the FBG. This paper explores the properties of various FBG designs in multimode and few-mode fibres, discusses the options for their modelling, and considers potential applications for astronomical instrumentation.

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: Not applicable · Consensus signal: none
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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