Comparing Quantitative Measures of Erythema, Pigmentation and Skin Response using Reflectometry
Bibliographic record
Abstract
We measured a number of pigmentation and skin response phenotypes in a sample of volunteers (n=397) living in State College, PA. The majority of this sample was composed of four groups based on stated ancestry: African-American, European-American, Hispanic and East Asian. Several measures of melanin concentration (L*, melanin index and adjusted melanin index) were estimated by diffuse reflectance spectroscopy and compared. The efficacy of these measures for assessing constitutive pigmentation and melanogenic dose-response was evaluated. Similarly, several measures of erythema (a*, erythema index and adjusted erythema index) were compared and evaluated in their efficacy in measuring erythema and erythemal dose-response. We show a high correspondence among all of the measures for the assessment of constitutive pigmentation and baseline erythema. However, our results demonstrate that evaluating melanogenic dose-response is highly dependent on the summary statistic used: while L* is a valid measure of constitutive pigmentation it is not an effective measure of melanogenic dose-response. Our results also confirm the use of a*, as it is shown to be highly correlated with the adjusted erythema index, a more advanced measure of erythema based on the apparent absorbance. Diffuse reflectance spectroscopy can be used to quantify the constitutive pigmentation, melanogenic dose-response at 7 d and erythemal dose-response at both 24 h and 7 d postexposure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".