Quantitative estimation of optical properties in bilayer media within the subdiffusive regime using tilted fiber-optic probe diffuse reflectance spectroscopy, part 2: probe design, realization, and experimental validation
Bibliographic record
Abstract
Significance: Tissues like skin have a layered structure where each layer's optical properties vary significantly. However, traditional diffuse reflectance spectroscopy assumes a homogeneous medium, often leading to estimations that reflects the properties of neither layer. There's a clear need for probes that can precisely measure the optical properties of layered tissues. Aim: This paper aims to design a diffuse reflectance probe capable of accurately estimating the optical properties of bilayer tissues in the subdiffusive regime. Approach: Using Monte Carlo simulations, we evaluated key geometric factors-fiber placement, tilt angle, diameter, and numerical aperture-on optical property estimation, following the methodology in Part I. A robust design is proposed that balances accurate intrinsic optical property (IOP) calculations with practical experimental constraints. Results: The designed probe, featuring eight illumination and eight detection fibers with varying spacings and tilt angles. The estimation error of the IOP calculation for bilayer phantoms is less than 20% for top layers with thicknesses between 0.2 and 1.0 mm. Conclusion: Building on the approach from Part I and using a precise calibration, the probe effectively quantified and distinguished the IOPs of bilayer samples, particularly those relevant to early skin pathology detection and characterization.
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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 | 0.000 |
| 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".