Bibliographic record
Abstract
In cold-exposed adult humans, significant or lethal decreases in body temperature are delayed by reducing heat loss via peripheral vasoconstriction and by increasing rates of heat production via shivering thermogenesis. This brief review focuses on the mechanisms of fuel selection responsible for sustaining long-term shivering thermogenesis. It provides evidence to explain large discrepancies in fuel selection measurements among shivering studies, and it proposes links between choices in fuel selection mechanism and human survival in the cold. Over the last decades, a number of studies have quantified the contributions of carbohydrate (CHO) and lipid to total heat generation. However, the exact contributions of these fuels still remain unclear because of large differences in fuel selection measurements even at the same metabolic rate. Recent advances on the mechanisms of fuel selection during shivering provide some plausible explanations for these discrepancies between shivering studies. This new evidence indicates that muscles can sustain shivering over several hours using a variety of fuel mixtures achieved by modifying diet (changing the size of CHO reserves) or by changing muscle fiber recruitment (increasing or decreasing the recruitment of type II fibers). From a practical perspective, how does the choice of fuel selection mechanism affect human survival in the cold? Based on a glycogen-depletion model, estimates of shivering endurance show that, whereas the oxidation of widely different fuel mixtures does not improve survival time, the selective recruitment of fuel-specific muscle fibers provides a substantial advantage for cold survival. By combining fundamental research on fuel metabolism and applied strategies to improve shivering endurance, future research in this area promises to yield important new information on what limits human survival in the cold.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".