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Record W3026401146 · doi:10.1364/ol.394637

Non-contact reflection-mode optical absorption spectroscopy using photoacoustic remote sensing

2020· article· en· W3026401146 on OpenAlexafffund
Kevan Bell, Parsin Haji Reza

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsIllumisonics (Canada)University of Waterloo
FundersCentre for Bioengineering and Biotechnology, University of WaterlooMitacsUniversity of WaterlooillumiSonicsCanada Foundation for InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsMaterials sciencePhotoacoustic spectroscopyOpacityAbsorption (acoustics)SpectroscopyReflection (computer programming)Absorption spectroscopyNanosecondWavelengthLaserPhotoacoustic effectTunable diode laser absorption spectroscopyTotal internal reflectionOptoelectronicsTunable laserPhysics

Abstract

fetched live from OpenAlex

A method of remote optical absorption spectroscopy is described that utilizes the photoacoustic remote sensing detection technique. A nanosecond tunable excitation source is used to excite thermo-elastic pressure-induced elasto-optic modulations within targets across a wide wavelength range from 210 to 680 nm, providing optical absorption contrast. These modulations are read remotely as back-reflected intensity variations within a continuous-wave 1310 nm detection beam. The absorption spectra of several samples including dyes and biological macromolecules are captured with an 8 mm working distance in reflection-mode without the use of containment chambers or acoustic detection. This represents an initial investigation into the characteristics of this technique, which may facilitate optical absorption measurement within previously inaccessible sample types due to their size or opacity.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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