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Record W2117030986 · doi:10.1109/lpt.2008.919457

Power-Referenced and Temperature-Calibrated Optical Fiber Refractometer

2008· article· en· W2117030986 on OpenAlexaff
Tuan Guo, Chengkun Chen, Albane Laronche, Jacques Albert

Bibliographic record

VenueIEEE Photonics Technology Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
Fundersnot available
KeywordsRefractometerRefractive indexFiber Bragg gratingCalibrationMaterials scienceOpticsOptical powerWavelengthOptical fiberTemperature measurementPower (physics)OptoelectronicsPhysicsLaser

Abstract

fetched live from OpenAlex

A novel technique for simultaneous measurement of refractive index and temperature based on power level detection of a weakly tilted fiber Bragg grating (TFBG) is proposed and experimentally demonstrated. Surrounding refractive index (RI) and temperature can be unambiguously determined by the TFBG transmitted and reflected power, respectively, avoiding complex wavelength interrogation. An accuracy approaching 1 x 10-4in terms of the RI as well as temperature self-calibration is shown for this robust and cost-effective refractometer.

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)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.206
Teacher spread0.197 · 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

Citations52
Published2008
Admission routes1
Has abstractyes

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