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Record W2569941192 · doi:10.1002/lpor.201600157

A true fiber optic refractometer

2017· article· en· W2569941192 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLaser & Photonics Review · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRefractometerOpticsRefractometryRefractive indexMaterials scienceOptical fiberWavelengthFiber Bragg gratingGraded-index fiberNormalized frequency (unit)Fiber optic sensorPhysics

Abstract

fetched live from OpenAlex

Abstract The best instrument to measure the refractive index of liquids is the Abbe refractometer which can only provide accuracies of the order of 10 −5 at visible wavelengths and 10 −4 in the near infrared. Here we present a technique by which the exact wavelength positions in the near infrared frequency comb of a tilted grating inscribed in the core of an optical fiber can be used to measure the absolute value of the refractive index of a liquid in which the fiber is inserted, with an accuracy of ±5×10 −5 . This is in contrast to typical fiber optic‐based “refractometry” where only refractive index variations can be measured accurately, hence the appellation of “true” fiber optic refractometer here. In addition to the increased accuracy, the fiber refractometer proposed here offers the additional advantages associated with in situ measurements. The performance of this refractometer is demonstrated by measurements in water from room temperature down to near freezing at wavelengths in the 1550 nm window. image

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.842
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)0.0010.002

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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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