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Record W4311686474 · doi:10.1117/12.2656209

Fabrication and spectral properties of two-dimensional fiber gratings based on LMA fiber

2022· article· en· W4311686474 on OpenAlexaff
Zonglun Che, Jun Wang, Pan Xu, Xijia Gu, Jing Zhu, Lina Ma, Chunyan Cao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceOpticsLong-period fiber gratingGraded-index fiberFiber Bragg gratingPolarization-maintaining optical fiberDispersion-shifted fiberPlastic optical fiberFiberPhotonic-crystal fiberGratingPHOSFOSFiber laserCore (optical fiber)Fiber optic sensorOptoelectronicsWavelengthPhysicsComposite material

Abstract

fetched live from OpenAlex

In this paper, the preparation and spectral properties of large mode field double-clad (LMA) fiber two-dimensional fiber gratings are studied. Firstly, a new compact grating analysis model for writing fiber gratings with different periods in different regions of the fiber core is theoretically proposed, and the spectral characteristics of the gratings written in different regions of the fiber core are studied based on the transfer matrix method. A three-wavelength two-dimensional fiber grating with a designed wavelength interval of 1.57 nm was successfully fabricated using a 248 nm excimer laser in a large mode field double-clad fiber laser. The research results show that the spectral characteristics of the two-dimensional fiber grating can be changed flexibly by changing the masking parameters, and the structure is compact. The experimentally obtained two-dimensional fiber grating spectrum is consistent with the theoretical analysis. The research results provide a theoretical reference for the design, fabrication and application of two-dimensional fiber gratings.

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 score0.002

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 integrity0.0000.000
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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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