Polarimetry in turbid, birefringent, optically active media: A Monte Carlo study of Mueller matrix decomposition in the backscattering geometry
Bibliographic record
Abstract
Determination of the intrinsic polarization properties of a complex turbid medium such as biological tissue in the backscattering geometry (a geometry that is convenient for in situ applications) is complicated due to the confounding influence of scattering, and due to simultaneous occurrence of several polarization effects. We have investigated the polar decomposition approach of Mueller matrices to delineate individual intrinsic polarization parameters (specifically linear retardance δ and optical rotation ψ) of a birefringent, chiral, turbid medium in the backscattering geometry, using Mueller matrices generated with polarization-sensitive Monte Carlo simulations. The results show that near the exact backscattering direction, the interplay of the scattering-induced linear retardance and diattenuation on the intrinsic values for δ and ψ is coupled in a complex interrelated way, due to contribution of the backscattered photons. In contrast, these effects were significantly reduced for detection positions at distances larger than a transport length away from the point of illumination. Simultaneous determination of the intrinsic values for δ and ψ of a birefringent, chiral, turbid medium in the backward detection geometry can thus be accomplished by decomposing the Mueller matrices recorded at distances larger than a transport length away from the point of illumination. Determination of the intrinsic values for these polarization parameters in backscattering geometry could be significant in, for example, for quantification of tissue structural anisotropy and for noninvasive blood glucose measurements of diabetic patients.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".