Maintaining Thermogenesis in Cold Exposed Humans: Relying on Multiple Metabolic Pathways
Bibliographic record
Abstract
Abstract In cold exposed humans, increasing thermogenic rate is essential to prevent decreases in core temperature. This review describes the metabolic requirements of thermogenic pathways, mainly shivering thermogenesis, the largest contributor of heat. Research has shown that thermogenesis is sustained from a combination of carbohydrates, lipids, and proteins. The mixture of fuels is influenced by shivering intensity and pattern as well as by modifications in energy reserves and nutritional status. To date, there are no indications that differences in the types of fuel being used can alter shivering and overall heat production. We also bring forth the potential contribution of nonshivering thermogenesis in adult humans via the activation of brown adipose tissue (BAT) and explore some means to stimulate the activity of this highly thermogenic tissue. Clearly, the potential role of BAT, especially in young lean adults, can no longer be ignored. However, much work remains to clearly identify the quantitative nature of this tissue's contribution to total thermogenic rate and influence on shivering thermogenesis. Identifying ways to potentiate the effects of BAT via cold acclimation and/or the ingestion of compounds that stimulate the thermogenic process may have important implications in cold endurance and survival. © 2014 American Physiological Society. Compr Physiol 4:1383‐1402, 2014.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".