Effect of training modality on inter‐individual differences in shivering pattern in humans
Bibliographic record
Abstract
Shivering electromyography in humans is characterized by two distinct types of myoelectric activity: continuous (4–8 Hz) and burst‐like (6–12 times/min), the latter displaying large inter‐individual differences (bursts/min) in men. The physiological reasons for this variation remain unclear. This study investigated the impact of two training modalities on shivering pattern. Two groups, endurance (E) and resistance (R) trained, were exposed to cold for 90 min using a liquid‐conditioned suit. The E trained group showed higher VO 2 peak and lower anaerobic capacity compared to the R trained group (E: 72.1 ± 2.9 mlO 2 /kg/min vs. R: 60.8 ± 1.4 mlO2/kg/min, P=0.007; Peak power R: 1277 ± 67 W vs. E: 949 ± 62 W, P=0.004). Core temperature was maintained throughout cold exposure in both groups. Skin temperature significantly decreased in both groups during cold exposure compared to baseline with no difference observed between groups (P < 0.0001). No difference was found between groups for heat production and shivering pattern during cold exposure. The variance of burst frequency was similar within each group, i.e. a similar inter‐individual variation was observed in both groups despite their respective training modality. In conclusion, training modality does not alter shivering pattern in men. This study was funded by NSERC.
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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) | 0.002 | 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".