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Record W2753068446 · doi:10.1117/1.jbo.22.8.087003

Accurate viscosity measurements of flowing aqueous glucose solutions with suspended scatterers using a dynamic light scattering approach with optical coherence tomography

2017· article· en· W2753068446 on OpenAlexafffund
Andrew Weatherbee, Ivan A. Popov, I. Alex Vitkin

Bibliographic record

VenueJournal of Biomedical Optics · 2017
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Waterloo
KeywordsOptical coherence tomographyViscosityOpticsMaterials scienceLight scatteringDynamic light scatteringScatteringStatic light scatteringMie scatteringBiomedical engineeringPhysicsNanotechnologyMedicineNanoparticleComposite material

Abstract

fetched live from OpenAlex

The viscosity of turbid colloidal glucose solutions has been accurately determined from spectral domain optical coherence tomography (OCT) M-mode measurements and our recently developed OCT dynamic light scattering model. Results for various glucose concentrations, flow speeds, and flow angles are reported. The relative "combined standard uncertainty" uc(η) on the viscosity measurements was ±1% for the no-flow case and ±5% for the flow cases, a significant improvement in measurement robustness over previously published reports. The available literature data for the viscosity of pure water and our measurements differ by 1% (stagnant case) and 1.5% (flow cases), demonstrating good accuracy; similar agreement is seen across the measured glucose concentration range when compared to interpolated literature values. The developed technique may contribute toward eventual noninvasive glucose measurements in medicine.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.268
Teacher spread0.232 · 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 teacher head, 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

Citations17
Published2017
Admission routes2
Has abstractyes

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