<title>Skin hydration imaging using a long-wavelength near-infrared digital camera</title>
Bibliographic record
Abstract
Skin hydration is a key factor in skin health. Hydration measurements can provide diagnostic information on the condition of skin and can indicate the integrity of the skin barrier function. Near-infrared spectroscopy measures the water content of living tissue by its effect on tissue reflectance at a particular wavelength. Imaging has the important advantage of showing the degree of hydration as a function of location. Short-wavelength (650-1050 nm) near infrared spectroscopic reflectance imaging has previously been used in-vivo to determine the relative water content of skin under carefully controlled laboratory conditions. We have recently developed a novel spectroscopic imaging system to acquire image sets in the long-wavelength region of the near infrared (960 to 1700 nm), where the water absorption bands are more intense. The LW-NIR systems uses a liquid- crystal tunable filter in front of the objective lens and incorporates a 12-bit digital camera with a 320-by-240-pixel indium-gallium arsenide array sensor. Custom software controls the camera and tunable filter, allowing image sets to be acquired and displayed in near-real time. Forearm skin hydration was measured in a clinical context using the long- wavelength imaging system, a short-wavelength imaging system, and non-imaging instrumentation. Among these, the LW-NIR system appears to be the most sensitive at measuring dehydration of skin.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.145 | 0.054 |
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".