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Record W2093194796 · doi:10.1063/1.2919796

Resolving cross sensitivity of fiber Bragg gratings with different polymeric coatings

2008· article· en· W2093194796 on OpenAlexaff
Ping Lü, Liqiu Men, Qiying Chen

Bibliographic record

VenueApplied Physics Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingPolyimideCoatingPHOSFOSPolymerSensitivity (control systems)AcrylateComposite materialOpticsStrain (injury)FiberOptical fiberPlastic optical fiberOptoelectronicsFiber optic sensorLayer (electronics)WavelengthElectronic engineering

Abstract

fetched live from OpenAlex

An approach to resolve the cross sensitivity of fiber Bragg gratings (FBGs) is proposed by the adoption of different polymers as the coating materials for gratings. From the different optical responses resulted from the gratings of different polymeric coatings, sensitivity to individual parameter can be exactly revealed. As an application of this approach, simultaneous discrimination of axial strain and temperature with two FBGs of different polymeric coatings is demonstrated with the axial strain and temperature sensitivities of 1.228pm∕με and 11.433pm∕°C for the acrylate-coated FBG, and 1.170pm∕με and 11.333pm∕°C for the polyimide-coated FBG, respectively.

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 score0.120
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 integrity0.0000.000
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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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

Citations57
Published2008
Admission routes1
Has abstractyes

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