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Record W1984451284 · doi:10.1364/oe.18.008135

Application of spectrum differential integration method in an in-line fiber Mach-Zehnder refractive index sensor

2010· article· en· W1984451284 on OpenAlexaff
Yi Li, Edouard Harris, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOpticsInterferometryRefractive indexWavelengthMach–Zehnder interferometerInterference (communication)Materials sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

A novel spectrum differential integration (SDI) method has been proposed and verified in an in-line fiber Mach-Zehnder (MZ) refractive index (RI) sensor using salt solutions. In SDI method, the difference between two interference spectra is determined by pointwise subtraction at each wavelength, followed by integration of the absolute differences along the scan range. Compared with the widely used peak wavelength shift method, the SDI method is more reliable over a wide wavelength range (on the order of 400 nm) and results in higher sensitivity as well as reduced device-dependence. The SDI method can also be utilized with other kinds of modal interferometric sensors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.745

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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.

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

Citations58
Published2010
Admission routes1
Has abstractyes

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