Multichannel spectroreflectometry: a noninvasive method for assessment of on-line hemoglobin derivatives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".