Body size and body composition effects on heat loss from the hands during severe cold exposure
Bibliographic record
Abstract
OBJECTIVES: This study investigated the influence of body size and composition on maintaining hand temperature during severe cold exposure. The hand's high surface area-to-volume ratio predisposes the hand to heat loss, increasing the risk of cold injury and even hypothermia, which are major selective pressures in cold environments. While vasoregulation may reduce heat loss from the hand, the effect of body form, tissue thermogenesis, and body insulation on heat loss is unknown. MATERIALS AND METHODS: Thermal imaging was used to determine heat loss during a 3-min ice-water hand immersion test carried out on 114 volunteers (female = 63, male = 51). Established anthropometric measures were used to quantify body size, and bioelectrical impedance analysis determined skeletal muscle and fat mass. RESULTS: Skeletal muscle mass relative to body mass was a highly significant predictor of heat loss, while body mass, fat mass, and stature were not. Body composition and body size had little to no significant influence during rewarming after immersion. DISCUSSION: The thermogenic properties of muscle mass support maintenance of hand temperature during severe cold exposure. The findings here suggest that muscular individuals are less susceptible to heat loss and cold injury, and may be better at manual tasks in cold conditions than nonmuscular individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".