Empirical modelling to predict the refractive index of human blood
Why this work is in the frame
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Bibliographic record
Abstract
Optical techniques used for the measurement of the optical properties of blood are of great interest in clinical diagnostics. Blood analysis is a routine procedure used in medical diagnostics to confirm a patient's condition. Measuring the optical properties of blood is difficult due to the non-homogenous nature of the blood itself. In addition, there is a lot of variation in the refractive indices reported in the literature. These are the reasons that motivated the researchers to develop a mathematical model that can be used to predict the refractive index of human blood as a function of concentration, temperature and wavelength. The experimental measurements were conducted on mimicking phantom hemoglobin samples using the Abbemat Refractometer. The results analysis revealed a linear relationship between the refractive index and concentration as well as temperature, and a non-linear relationship between refractive index and wavelength. These results are in agreement with those found in the literature. In addition, a new formula was developed based on empirical modelling which suggests that temperature and wavelength coefficients be added to the Barer formula. The verification of this correlation confirmed its ability to determine refractive index and/or blood hematocrit values with appropriate clinical accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it