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Record W2154116390 · doi:10.1109/jsen.2006.874494

High-strength fiber Bragg gratings for a temperature-sensing array

2006· article· en· W2154116390 on OpenAlexaff
Xijia Gu, Ling Guan, Yifeng He, Huifu Zhang, Rebecca Herman

Bibliographic record

VenueIEEE Sensors Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingFabricationOptoelectronicsLaserOptical fiberOpticsCoatingTemperature measurementComposite materialWavelength

Abstract

fetched live from OpenAlex

We have successfully demonstrated a one-step laser process of fabricating fiber Bragg grating arrays directly through fiber buffer. A new polysiloxane-based buffer provides high 244-nm transmission and showed no degradation due to UV irradiation or thermal annealing as verified by Weibull analysis of tension tests. The FBG array can withstand over 400-kpsi tension tests and 220/spl deg/C temperature. The spectral quality of the FBG is compatible with the FBGs currently used by the sensing industry and, therefore, can be interrogated with existing instruments. The laser fabrication process is robust with no coating, stripping, or recoating required.

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 categoriesMeta-epidemiology (narrow)
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 score0.451
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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.

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

Citations16
Published2006
Admission routes1
Has abstractyes

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