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Record W2052510160 · doi:10.3390/s100301716

Chemical Sensing Using Fiber Cavity Ring-Down Spectroscopy

2010· review· en· W2052510160 on OpenAlex
Helen Waechter, Jessica Litman, Adrienne H. Cheung, Jack A. Barnes, Hans‐Peter Loock

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

VenueSensors · 2010
Typereview
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityOntario Centres of ExcellenceEli Lilly and CompanySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsFiberCavity ring-down spectroscopyAnalyteRefractive indexOptical fiberAttenuationSpectroscopyChemical sensorMaterials scienceAbsorption (acoustics)Fiber optic sensorAbsorption spectroscopyRing (chemistry)OpticsAnalytical Chemistry (journal)OptoelectronicsChemistryComposite materialChromatography

Abstract

fetched live from OpenAlex

Waveguide-based cavity ring-down spectroscopy (CRD) can be used for quantitative measurements of chemical concentrations in small amounts of liquid, in gases or in films. The change in ring-down time can be correlated to analyte concentration when using fiber optic sensing elements that change their attenuation in dependence of either sample absorption or refractive index. Two types of fiber cavities, i.e., fiber loops and fiber strands containing reflective elements, are distinguished. Both types of cavities were coupled to a variety of chemical sensor elements, which are discussed and compared.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.298
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