An investigation on the use of data-driven scattering profiles in Monte Carlo simulations of ultraviolet light propagation in skin tissues
Bibliographic record
Abstract
Ultraviolet light can affect the appearance and medical condition of the human skin by triggering biophysical processes such as erythema, melanogenesis, photoaging and carcinogenesis. The evolution of these processes is related to the amount of ultraviolet light absorbed by skin pigments. This amount may vary with the wavelength and path length of the radiation that is propagated within the skin tissues. For many years, biomedical researchers have been investigating the propagation of ultraviolet light in skin tissues through Monte Carlo simulations. The scattering of the incident radiation by tissue internal structures, a key component in this process, is usually approximated by functions without a plausible connection with the underlying physical phenomena. In this paper, we examine the origins of such an approach, and question its generalized use with respect to wavelengths and biological materials for which there is no supporting data available. Furthermore, we perform comparisons to demonstrate that the accuracy and predictability of Monte Carlo simulations of ultraviolet propagation in skin tissues can be improved by using a data-driven approach to represent the scattering profile of these tissues.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".