Oxidative fuel selection and shivering thermogenesis during a 12- and 24-h cold-survival simulation
Bibliographic record
Abstract
Because the majority of cold exposure studies are constrained to short-term durations of several hours, the long-term metabolic demands of cold exposure, such as during survival situations, remain largely unknown. The present study provides the first estimates of thermogenic rate, oxidative fuel selection, and muscle recruitment during a 24-h cold-survival simulation. Using combined indirect calorimetry and electrophysiological and isotopic methods, changes in muscle glycogen, total carbohydrate, lipid, protein oxidation, muscle recruitment, and whole body thermogenic rate were determined in underfed and noncold-acclimatized men during a simulated accidental exposure to 7.5 °C for 12 to 24 h. In noncold-acclimatized healthy men, cold exposure induced a decrease of ∼0.8 °C in core temperature and a decrease of ∼6.1 °C in mean skin temperature (range, 5.4-6.9 °C). Results showed that total heat production increased by approximately 1.3- to 1.5-fold in the cold and remained constant throughout cold exposure. Interestingly, this constant rise in Ḣprod and shivering intensity was accompanied by a large modification in fuel selection that occurred between 6 and 12 h; total carbohydrate oxidation decreased by 2.4-fold, and lipid oxidation doubled progressively from baseline to 24 h. Clearly, such changes in fuel selection dramatically reduces the utilization of limited muscle glycogen reserves, thus extending the predicted time to muscle glycogen depletion to as much as 15 days rather than the previous estimates of approximately 30-40 h. Further research is needed to determine whether this would also be the case under different nutritional and/or colder conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".