Effects of the menstrual cycle on muscle recruitment and oxidative fuel selection during cold exposure
Bibliographic record
Abstract
Differences in core temperature and body heat content, generally observed between the luteal and follicular phase of the menstrual cycle, have been reported to modulate the thermogenic activity of cold-exposed women. However, it is unclear how this change in whole body shivering activity will influence fuel selection. The goal of this study was to quantify the effects of the menstrual cycle on muscle recruitment and oxidative fuel selection during low-intensity shivering. Electromyographic activity of eight large muscles was monitored while carbohydrate, lipid, and protein utilization was simultaneously quantified in the follicular and luteal phases of the menstrual cycle in nonacclimatized women shivering at a low intensity. The onset (∼25 min), intensity (∼15% of maximal voluntary contraction), and pattern (∼6 shivering bursts/min) of the shivering response did not differ between menstrual cycle phases, regardless of differences in core temperature and hormone levels. This resulted in lipids remaining the predominant substrate, contributing 75% of total heat production, independent of menstrual phase. We conclude that hormone fluctuations inherent in the menstrual cycle do not affect mechanisms of substrate utilization in the cold. Whether the large contribution of lipids to total heat production in fuel selection confers a survival advantage remains to be established.
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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 | 0.001 |
| 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".