Energy balance and body composition during US Army special forces training
Bibliographic record
Abstract
Small Unit Tactics (SUT) is a 64-day phase of the Special Forces Qualification Course designed to simulate real-world combat operations. Assessing the metabolic and physiological responses of such intense training allows greater insights into nutritional requirements of soldiers during combat. The purpose of this study was to examine energy balance around specific training events, as well as changes in body mass and composition. Data were collected from 4 groups of soldiers (n = 36) across 10-day periods. Participants were 28 ± 5 years old, 177 ± 6 cm tall, and weighed 83 ± 7 kg. Doubly labeled water (D2(18)O) was used to assess energy expenditure. Energy intake was calculated by subtracting energy in uneaten foods from known energy in distributed foods in individually packaged combat rations or in the dining facility. Body composition was estimated from skinfold thickness measurements on days 0 and 64 of the course. Simulated urban combat elicited that largest energy deficit (11.3 ± 2.3 MJ·day(-1) (2700 ± 550 kcal·day(-1)); p < 0.05), and reduction in body mass (3.3 ± 1.9 kg; p < 0.05), during SUT, while energy balance was maintained during weapons familiarization training and platoon size raids. Over the entire course body mass decreased by 4.2 ± 3.7 kg (p < 0.01), with fat mass decreasing by 2.8 ± 2.0 kg (p < 0.01) and fat-free mass decreasing by 1.4 ± 2.8 kg (p < 0.05). The overall reduction in body mass suggests that soldiers were in a negative energy balance during SUT, with high energy deficit being observed during strenuous field training.
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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".