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Record W2058941240 · doi:10.1139/v10-006

Absorption detection using optical waveguide cavities

2010· article· en· W2058941240 on OpenAlexafffundvenue
Hans‐Peter Loock, Jack A. Barnes, G. Gagliardi, Runkai Li, Richard D. Oleschuk, Helen Wächter

Bibliographic record

VenueCanadian Journal of Chemistry · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsQueen's University
FundersMinistero dell’Istruzione, dell’Università e della RicercaQueen's UniversityConsiglio Nazionale delle RicercheOntario Centres of ExcellenceEli Lilly and Company
KeywordsFinesseCavity ring-down spectroscopyAbsorption (acoustics)WaveguideOptical cavityChemistryRefractive indexSpectroscopyOptoelectronicsAbsorption spectroscopyOpticsOptical fiberPhase (matter)WavelengthMaterials scienceFabry–Pérot interferometerLaserPhysics

Abstract

fetched live from OpenAlex

Cavity ring-down spectroscopy is a spectroscopic method that uses a high quality optical cavity to amplify the optical loss due to the light absorption by a sample. In this presentation we highlight two applications of phase-shift cavity ring-down spectroscopy that are suited for absorption measurements in the condensed phase and make use of waveguide cavities. In the first application, a fiber loop is used as an optical cavity and the sample is introduced in a gap in the loop to allow absorption measurements of nanoliters of solution at the micromolar level. A second application involves silica microspheres as high finesse cavities. Information on the refractive index and absorption of a thin film of ethylene diamine on the surface of the microresonator is obtained simultaneously by the measurements of the wavelength shift of the cavity mode spectrum and the change in optical decay time, 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)0.001
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 score0.003

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 studies0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.186 · 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

Citations11
Published2010
Admission routes3
Has abstractyes

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