Bibliographic record
Abstract
Heat exchange has been thoroughly studied in cold-exposed humans, but the metabolic substrates used for thermogenesis have received less attention. This review deals with oxidative fuel selection in shivering humans. Lipids provide most of the heat during low-intensity shivering, whereas carbohydrates become dominant under more extreme cold conditions. The contribution from plasma glucose always remains minor, but muscle glycogen plays an important role during intense shivering. Whether the size of muscle glycogen stores influences endurance in the cold remains to be demonstrated. The fuel selection patterns of shivering and exercise are different, but the mechanisms underlying this difference have not been investigated. The simultaneous measurement of metabolic substrate oxidation and muscle fibre recruitment has allowed to characterize two different mechanisms of fuel selection in shivering humans: the recruitment of different pathways within the same fibres and of different fuel-specific fibres within the same muscles. This suggests that muscle fibre composition of each individual may affect survival. Future research promises to provide a combination of theoretical advances on fundamental principles of fuel selection and applied strategies to manipulate fibre composition (through training) or fuel metabolism (through diet) to prolong human survival in cold environments.
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) | 0.001 | 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 | 0.001 | 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".