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Record W2127526242 · doi:10.1364/oe.18.005580

A fiberoptic reflectance probe with multiple source-collector separations to increase the dynamic range of derived tissue optical absorption and scattering coefficients

2010· article· en· W2127526242 on OpenAlexaff
Anthony Kim, Mathieu Roy, Farhan Dadani, Brian C. Wilson

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer Research
FundersNational Institute of Neurological Disorders and Stroke
KeywordsOpticsMonte Carlo methodAttenuation coefficientDiffuse reflectionScatteringAbsorption (acoustics)Imaging phantomMaterials scienceDiffusionDynamic rangeDiffuse reflectance infrared fourier transformRange (aeronautics)PhysicsChemistryMathematics

Abstract

fetched live from OpenAlex

Measurement of tissue optical absorption and (transport) reduced scattering coefficients (mu(a) and mu(s)', respectively) is fundamental to many applications of light in medicine and biology. We report a handheld fiberoptic probe to determine these coefficients by measuring the diffuse reflectance at multiple source-collector distances, which allows for a larger dynamic range than a single source-collector separation. Diffusion theory and a priori knowledge of the spectral shape of mu(a) and mu(s)' are used in a forward model of the diffuse reflectance. The dynamic range and accuracy of this method were evaluated using Monte Carlo simulations, phantom experiments and tissues in vivo.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.295
Teacher spread0.286 · 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

Citations102
Published2010
Admission routes1
Has abstractyes

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