The effects of environmental and physical stress on energy expenditure, energy intake, and appetite
Bibliographic record
Abstract
Body weight loss occurs frequently in military personnel engaged in field operations. When this weight loss is rapid, or extensive it is associated with health and performance decrements. While the nature of military work does not allow for energy expenditure (EE) to be freely altered, energy intake (EI) can be increased to match EE and prevent weight loss. Therefore a primary objective of the current dissertation was to develop a physiological and empirical basis to facilitate informed estimates of the EI that would be required to offset the EE demand of military tasks during field operations. Three different approaches were undertaken: 1) The energy costs of 46 infantry tasks were measured; the results should reduce the dependency on less accurate predictions. 2) The impact of ambient temperature on EE of the tasks was minimal (~3%) when the ambient temperature was between -10°C and 30°C. This indicates that caloric supplementation of field rations on account of temperature is likely unnecessary during short-term operations occurring within this temperature range; and, lastly 3) A simple algorithm based on accelerometry and heart rate was developed to assess EE in the field. The application of this algorithm should improve EE/EI matching. Unfortunately military personnel engaged in arduous field operations usually experience an energy deficit, even when food availability is adequate. Voluntary anorexia can ultimately thwart nutrition optimization in the field, therefore the role of appetite was also explored. While hormonal responses pointed towards appetite suppression with increased physical activity levels (with a partial blunting of that response in the cold) and subjective appetite was the lowest in the heat and highest in the cold, actual EI was unchanged regardless of ambient temperature or whether the participant was sedentary or active. This research demonstrated that even in the most favourable scenarios military personnel engaged in typical infantry tasks may under eat and plunge into a negative energy balance. These results suggest that factors other than food availability and palatability, such as policy or procedural changes should be considered in addressing voluntary anorexia in the field in military personnel.
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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 | 0.001 | 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".