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Record W2165831249 · doi:10.1109/jlt.2009.2038802

Temperature Insensitive All-Fiber Compact Polarization-Maintaining Photonic Crystal Fiber Based Interferometer and Its Applications in Fiber Sensors

2010· article· en· W2165831249 on OpenAlexaff
Bo Dong, Da-Peng Zhou, Li Wei

Bibliographic record

VenueJournal of Lightwave Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
Fundersnot available
KeywordsPhotonic-crystal fiberMaterials scienceInterferometryOpticsPolarization-maintaining optical fiberGraded-index fiberCladding (metalworking)Optical fiberSingle-mode optical fiberPlastic optical fiberCladding modeFiber optic sensorOptoelectronicsMicrostructured optical fiberRefractive indexDispersion-shifted fiberPhysics

Abstract

fetched live from OpenAlex

A novel temperature insensitive all-fiber compact polarization-maintaining photonic crystal fiber (PMPCF) based interferometer was presented. The coupling between the PMPCF and single mode fiber (SMF) was investigated experimentally and analytically. With an all-fiber compact SMF-PMPCF-SMF structure, the cladding modes propagating in the PMPCF can be effectively excited by finely core-offsetting one splice between the PMPCF and SMF. With the increase of the core-offset, the interference depth of the interference pattern enlarges accordingly. Under a suitable core-offset, the PMPCF based interferometer with high interference depth can be obtained. Moreover, it is temperature insensitive due to its ultra-low thermal characteristics. We also demonstrated its applications in strain and refractive index measurement. The sensors have the advantages of small size, simple and compact all fiber structure, high sensitivity, and temperature insensitiveness.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.233
Teacher spread0.227 · 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

Citations90
Published2010
Admission routes1
Has abstractyes

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