Skin Temperature: The Impact of Perfusion, Epidermis Thickness, and Skin Wetness
Bibliographic record
Abstract
This work aimed to elucidate the primary factors which affect skin temperature. A simple thermophysical model of the skin, which accounts for radiative, convective, and evaporative heat losses, has been developed to address it. The model is based on the skin’s morphology and consists of passive (nonviable tissue) and active (viable tissue) layers. The bioheat equation was solved for these layers using realistic assumptions. It was found that other than the ambient temperature, blood perfusion and epidermis thickness are the primary factors responsible for the skin temperature variations. The main temperature drop in the skin is attributed to the cooling of the blood in the venous plexus. The temperature drop in the epidermis is on the scale of 0.1 °C for the normal epidermis but can be 1.5–2 °C or higher in calluses. Thus, local skin temperature variations can indicate the epidermis thickness variations, particularly in callus-prone areas. The effects of relative air humidity and skin wetness on skin temperature were also quantified. The presence of free moisture on the skin (e.g., wet wound) significantly increases the heat transfer, resulting in a skin temperature drop, which can be on the scale of several degrees Celsius. The relative air humidity significantly contributes (by slowing heat dissipation) only in the case of evaporative heat loss from wet skin. Therefore, wet skin is undesirable and should be avoided during a thermographic assessment.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".