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Record W2125502300 · doi:10.1109/lpt.2007.909669

Self-Referencing in Optical-Fiber Surface Plasmon Resonance Sensors

2007· article· en· W2125502300 on OpenAlexaff
Zhiyi Zhang, Ping Zhao, Fengguo Sun, Gaozhi Xiao, Yaming Wu

Bibliographic record

VenueIEEE Photonics Technology Letters · 2007
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaInstitute for Microstructural Sciences
Fundersnot available
KeywordsSurface plasmon resonanceMaterials scienceOptical fiberFiber optic sensorOptoelectronicsOpticsResonance (particle physics)Surface plasmonFiberPlasmonNanotechnologyPhysics

Abstract

fetched live from OpenAlex

We have achieved a self-referencing function in optical-fiber surface plasmon resonance (SPR) sensors by fabricating two tandem SPR sections in a single optical fiber and by coating one of the sections with a reference material to create an additional resonance at a longer wavelength for referencing, while keeping the other one for regular sensing. By choosing a reference material with a very large thermooptic coefficient, we have demonstrated an SPR optical fiber sensor that can, by itself, correct the effect of temperature variation in the testing medium.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.210
Teacher spread0.204 · 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

Citations34
Published2007
Admission routes1
Has abstractyes

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