Effects of winter military training on energy balance, whole-body protein balance, muscle damage, soreness, and physical performance
Bibliographic record
Abstract
Physiological consequences of winter military operations are not well described. This study examined Norwegian soldiers (n = 21 males) participating in a physically demanding winter training program to evaluate whether short-term military training alters energy and whole-body protein balance, muscle damage, soreness, and performance. Energy expenditure (D2(18)O) and intake were measured daily, and postabsorptive whole-body protein turnover ([(15)N]-glycine), muscle damage, soreness, and performance (vertical jump) were assessed at baseline, following a 4-day, military task training phase (MTT) and after a 3-day, 54-km ski march (SKI). Energy intake (kcal·day(-1)) increased (P < 0.01) from (mean ± SD (95% confidence interval)) 3098 ± 236 (2985, 3212) during MTT to 3461 ± 586 (3178, 3743) during SKI, while protein (g·kg(-1)·day(-1)) intake remained constant (MTT, 1.59 ± 0.33 (1.51, 1.66); and SKI, 1.71 ± 0.55 (1.58, 1.85)). Energy expenditure increased (P < 0.05) during SKI (6851 ± 562 (6580, 7122)) compared with MTT (5480 ± 389 (5293, 5668)) and exceeded energy intake. Protein flux, synthesis, and breakdown were all increased (P < 0.05) 24%, 18%, and 27%, respectively, during SKI compared with baseline and MTT. Whole-body protein balance was lower (P < 0.05) during SKI (-1.41 ± 1.11 (-1.98, -0.84) g·kg(-1)·10 h) than MTT and baseline. Muscle damage and soreness increased and performance decreased progressively (P < 0.05). The physiological consequences observed during short-term winter military training provide the basis for future studies to evaluate nutritional strategies that attenuate protein loss and sustain performance during severe energy deficits.
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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".