Quantification of Absorber through a Scattering Medium of Different Thickness Using Evanescent Light Piping
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Bibliographic record
Abstract
A non-invasive method has been developed for analyte quantification in fluids surrounded by optically-scattering, opaque walls. This method is based on steady state, visible wavelength reflectance measurements made simultaneously at multiple positions on the surface of a sample. Previous work has shown that reflectance measurements contain information about underlying scattering layers in layered scattering samples. We hypothesise that similar information about an absorbing layer below a scattering layer can be obtained from evanescent wave effects. Principal component analysis showed the data to be composed of three components, which were refined by a multivariate curve resolution alternating least squares (MCR-ALS) approach with non-negativity constraints. The first component is related to the scattering layer thickness, the second is associated with analyte concentration and the third is due to a minor back reflection within the sample cell. Both MCR and stagewise multi-linear regression (SMLR) approaches were taken to estimate analyte concentration and scattering layer thickness, for samples having thicknesses between 1 mm and 8 mm. Results demonstrate that a simple experimental configuration can easily predict optical properties of unknown samples. With the adoption of a multi-wavelength approach to this method, it is expected that improved absorption coefficient (μ a ) estimation accuracy can be realised in a variety of application areas such as in analysis through opaque containers, in vivo measurements and in-line monitoring of reactions.
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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) | 0.001 | 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 | 0.001 |
| 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 it