Detecting axial heterogeneity of birefringence in layered turbid media using polarized light imaging
Bibliographic record
Abstract
The structural anisotropy of biological tissues can be quantified using polarized light imaging in terms of birefringence; however, birefringence varies axially in anisotropic layered tissues. This may present ambiguity in result interpretation for techniques whose birefringence results are averaged over the sampling volume. To explore this issue, we extended the polarization sensitive Monte Carlo code to model bi-layered turbid media with varying uniaxial birefringence in the two layers. Our findings demonstrate that the asymmetry degree (ASD) between the off-diagonal Mueller matrix elements of heterogeneously birefringent samples is higher than the homogenously birefringent (uniaxial) samples with the same effective retardance (magnitude and orientation). We experimentally verified the validity of ASD as a birefringence heterogeneity measure by performing polarized light measurements of bi-layered elastic and scattering polyacrylamide phantoms.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".