Effects of carbohydrate availability on sustained shivering II. Relating muscle recruitment to fuel selection
Bibliographic record
Abstract
The purpose of this study was to quantify how shivering activity would be affected by large changes in fuel metabolism (see Haman F, Peronnet F, Kenny GP, Doucet E, Massicotte D, Lavoie C, and Weber J-M, J Appl Physiol 96: 000-000, 2004). Adult men were exposed to 10 degrees C for 2 h after a low-carbohydrate diet and exercise (Lo) and after high-carbohydrate diet without exercise (Hi). Using simultaneous metabolic and electromyographic (EMG) measurements, we quantified the effects of changes in fuel selection on the shivering activity of eight large muscles representing >90% of total shivering muscle mass. Contrary to expectation, drastic changes in fuel metabolism [carbohydrates 28 vs. 65% of total heat production (Hprod), lipids 53 vs. 23% Hprod, and proteins 19 vs. 12% Hprod for Lo and Hi, respectively] are achieved without altering the EMG signature of shivering muscles. Results show that total shivering activity and the specific contribution of each muscle to total shivering activity are not affected by large changes in fuel selection. In addition, we found that changes in burst shivering rate ( approximately 4 bursts/min), relative contribution of burst activity to total shivering ( approximately 10% of total shivering activity), and burst shivering intensity ( approximately 12% of maximal voluntary contraction) are the same between Lo and Hi. Spectral analysis of EMG signals also reveals that mean frequencies of the power spectrum remained the same under all conditions (whole body average of 78 +/- 5 Hz for Lo and 83 +/- 7 Hz for Hi). During low-intensity shivering, humans are therefore able to sustain the same thermogenic rate by oxidizing widely different fuel mixtures within the same muscle fibers.
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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) | 0.000 | 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) | 0.000 | 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".