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Record W3150912815 · doi:10.1364/oe.430403

Hyperspectral absorption microscopy using photoacoustic remote sensing

2021· article· en· W3150912815 on OpenAlexafffund
Kevan Bell, Lyazzat Mukhangaliyeva, Layla Khalili, Parsin Haji Reza

Bibliographic record

VenueOptics Express · 2021
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
KeywordsHyperspectral imagingMaterials scienceOpticsAbsorption (acoustics)Photoacoustic spectroscopyChemical imagingMicroscopyOptoelectronicsLaserRemote sensingPhysics

Abstract

fetched live from OpenAlex

An improved method of remote optical absorption spectroscopy and hyperspectral optical absorption imaging is described which takes advantage of the photoacoustic remote sensing detection architecture. A wide collection of photoacoustic excitation wavelengths ranging from 210 nm to 1550 nm was provided by a nanosecond tunable source allowing access to various salient endogenous chromophores such as DNA, hemeproteins, and lipids. Sensitivity of the device was demonstrated by characterizing the infrared absorption spectrum of water. Meanwhile, the efficacy of the technique was explored by recovering cell nuclei and oxygen saturation from a live chicken embryo model and by recovering adipocytes from freshly resected murine adipose tissue. This represents a continued investigation into the characteristics of the hyperspectral photoacoustic remote sensing technique which may represent an effective means of non-destructive endogenous contrast characterization and visualization.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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

Citations23
Published2021
Admission routes2
Has abstractyes

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