Examining the Impact of Sample Thickness Variations on the Hyperspectral Radiometric Responses of Flowing Blood
Bibliographic record
Abstract
The hyperspectral reflectance and transmittance of flowing blood samples are employed in a wide range of biomedical applied research initiatives such as the detection and monitoring of hematological abnormalities. The success of these initiatives is tied to the correct interpretation of these radiometric quantities. This, in turn, requires a comprehensive understanding about their sensitivity to variations in the experimental conditions in which they have been obtained. In this paper, we aim to contribute to these efforts by systematically examining the effects of sample thickness variations on these quantities. More specifically, we employed controlled in silico experiments to assess these effects on samples with different biophysical characteristics, notably their hematocrit, hemolysis level and orientation of their constituent cells with respect to the flow direction. To ensure a high degree of fidelity in our experiments, we used a first-principles simulation framework supported by measured data. Our findings unveil distinct spectrally-dependent trends associated with reflectance and transmittance changes elicited by sample thickness variations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".