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Record W1963702954 · doi:10.1364/ao.50.004257

Interrogation technique for TFBG-SPR refractometers based on differential orthogonal light states

2011· article· en· W1963702954 on OpenAlexaff
Valérie Voisin, Christophe Caucheteur, Patrice Mégret, Jacques Albert

Bibliographic record

VenueApplied Optics · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
Fundersnot available
KeywordsRefractive indexOpticsMaterials scienceRefractometrySurface plasmon resonanceRefractometerDemodulationAmplitudeLight scatteringRayRayleigh scatteringOptoelectronicsScatteringPhysicsTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

The generation of near-IR surface plasmon resonance in gold-coated tilted fiber Bragg gratings is strongly dependent on both the polarization state of the transmission light and the property of confining materials (including the coating materials and surrounding media). These dependencies can be advantageously used to demodulate the amplitude spectrum and retrieve the surrounding refractive index. In this paper, we present an automated demodulation technique that measures the surrounding refractive index by comparing the differential amplitude of resonance peaks near the plasmon attenuation for two orthogonal amplitude spectra recorded in the same operating conditions. A mean sensitivity of more than 500 nm per refractive index unit is reported. This new refractive index measurement method is shown to be accurate to 5×10(-5) over a full range of 0.01 in water solutions.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.016
GPT teacher head0.222
Teacher spread0.206 · 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

Citations41
Published2011
Admission routes1
Has abstractyes

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