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Record W2062682074 · doi:10.1063/1.3115029

Tapered fiber Mach–Zehnder interferometer for simultaneous measurement of refractive index and temperature

2009· article· en· W2062682074 on OpenAlexaff
Ping Lü, Liqiu Men, Kevin Sooley, Qiying Chen

Bibliographic record

VenueApplied Physics Letters · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMach–Zehnder interferometerInterferometryRefractive indexOpticsBlueshiftMaterials scienceAttenuationStep-index profileGraded-index fiberInterference (communication)Optical fiberWavelengthNormalized frequency (unit)FiberOptoelectronicsFiber optic sensorPhysicsTelecommunications

Abstract

fetched live from OpenAlex

An approach to achieve simultaneous measurement of refractive index and temperature is proposed by using a Mach–Zehnder interferometer realized on tapered single-mode optical fiber. The attenuation peak wavelength of the interference with specific order in the transmission spectrum shifts with changes in the environmental refractive index and temperature. By utilizing S-band and C/L-band light sources, simultaneous discrimination of refractive index and temperature with the tapered fiber Mach–Zehnder interferometer is demonstrated with the corresponding sensitivities of −23.188 nm/RIU (refractive index unit) and 0.071 nm/ °C, and −26.087 nm/RIU (blueshift) and 0.077 nm/°C (redshift) for the interference orders of 169 and 144, 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 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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations560
Published2009
Admission routes1
Has abstractyes

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