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Record W2155100281 · doi:10.1364/ao.48.000d52

Multichannel spectroreflectometry: a noninvasive method for assessment of on-line hemoglobin derivatives

2009· article· en· W2155100281 on OpenAlexaff
Vasile Diaconu

Bibliographic record

VenueApplied Optics · 2009
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsReflectometryLambdaOpticsPhysicsAbsorption (acoustics)HemoglobinLight scatteringAbsorbanceFunction (biology)ScatteringChemistryTime domainComputer scienceBiology

Abstract

fetched live from OpenAlex

The goal of the current study was to introduce a mathematical method to derive hemoglobin, oxyhemoglobin and carboxyl-hemoglobin absorption factors from full spectrum reflectometry measurements of retinal microcapillaries. The mathematical equation that describes the spectral reflectometry function was expressed as a linear combination of several terms of S(i)(lambda) representing the spectral signature functions of hemoglobin, oxyhemoglobin, carboxyl-hemoglobin, ocular media, melanin, and a scattering factor. Contrary to the classical model, where the reflectometry function was expressed as an absorbance Ab(lambda)=log?(incident light(lambda)/reflected light(lambda)), in this model and system, it is proposed to express the reflectometry function from the eye structures as an absorption factor A(lambda)%=incident light(lambda)/reflected light(lambda). To increase confidence in the estimation of hemoglobin derivatives, the mathematical model was applied to only a part of the spectral function of reflectometry, while the results of the model were used to explain the other part of the reflectometry function. The results demonstrate that for the visible spectral field, the model that explains the absorption of the light by the blood contained in the microcapillaries of biological structures is not compatible with the Beer-Lambert law.

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)0.000
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.453
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.429
Teacher spread0.382 · 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
GenreMethods

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

Citations20
Published2009
Admission routes1
Has abstractyes

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