Near‐IR spectroscopic imaging for skin hydration: The long and the short of it
Bibliographic record
Abstract
Near-IR spectroscopic methods have been developed to determine the degree of hydration of human skin in vivo. Noncontact reflectance spectroscopic imaging was used to investigate the distribution of skin moisture as a function of location. A human study in a clinical setting has generated quantitative data showing the effects of a drying agent and a moisturizer on delineated regions of the forearms of eight volunteers. Two digital imaging systems equipped with liquid-crystal tunable filters were used to collect stacks of monochromatic images at 10-nm intervals over the 650-1050 and 960-1700 nm wavelength bands. Synthetic images generated from measurements of water absorption band areas at three different near-IR wavelengths (970, 1200, and 1450 nm) showed obvious differences in the apparent distribution of water in the skin. Changes resulting from the skin treatments were much more evident in the long-wavelength images than in the short-wavelength ones. The variable sensitivity of the method at different wavelengths has been interpreted as being the result of different penetration depths of the IR light used in the reflectance studies.
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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 | 0.000 | 0.002 |
| Open science | 0.000 | 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".