Daily energy balance and eating behaviour during a 14-day cold weather expedition in Greenland
Bibliographic record
Abstract
We assessed energy compensation, appetite, and reward value of foods during a 14-day military expedition in Greenland realized by 12 male French soldiers, during which energy compensation was optimized by providing them with easy-to-eat palatable foods in excess. Although daily energy expenditure (estimated by accelerometry) stayed relatively constant throughout the expedition (15 ± 9 MJ·day−1), energy intake (EI; estimated by self-reported diaries) was 17% higher during the D8–D14 period compared with the D1–D7 period, leading to a neutral energy balance (EB). Body fat mass (BFM) significantly decreased (–1.0 ± 0.7 kg, p < 0.001) but not body mass (BM). Neither hunger scores (assessed by visual analog scales) nor components of the reward value of food (explicit liking (EL) and food preference) were significantly altered. However, changes in EL at D10 were positively correlated with changes in BM (r = 0.600, p < 0.05) and BFM (r = 0.680, p < 0.05) and changes in hunger in the EI of the relevant period (r = 0.743, p < 0.01 for D1–D7, r = 0.652, p < 0.05 for D8–14). This study shows that the negative EB and BM loss can be attenuated by an appropriate food supply and that subjective components of eating behaviour, such as hunger and EL, may be useful to predict the magnitude of energy compensation. Novelty Energy intake increases during of a 14-day expedition in the cold. Energy compensation was likely facilitated by providing participants with easy-to-eat palatable and familiar foods. Hunger scores and EL for energy-dense foods were associated with high EIs and low BM changes.
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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".