Energy Intake, Basal Metabolic Rate, and Within-Individual Trade-Offs in Men and Women Training for a Half Marathon: A Reanalysis
Bibliographic record
Abstract
Understanding the mechanisms governing energy budgets during periods of high energy expenditure is important from both an evolutionary physiology and behavioral ecology perspective. In particular, we still know too little about the linkages between key components such as daily energy expenditure (DEE), daily energy intake (DEI), and basal metabolic rate (BMR). Westerterp et al. repeatedly measured DEI (self-reported), BMR (respirometry), and body composition (fat mass) in 23 adult subjects as they transitioned from an inactive lifestyle and trained during 44 wk in preparation of running a half marathon. Here, I reanalyzed this data set using bivariate mixed models to partition the phenotypic correlation between DEI and BMR at the among- and within-individual levels. Reported DEI and BMR were positively correlated at the among-individual level (i.e., individuals with high average reported DEI also have a high average BMR). However, reported DEI and BMR were not correlated within individuals. There was a negative within-individual relationship between BMR and surplus energy (i.e., the energy intake above BMR = DEI - BMR), suggesting the presence of compensation mechanisms between BMR and other energy-demanding activities occurring within individuals. Thus, the principles governing energy budget were different at the among- and within-individual levels. To the extent that this situation is applicable to wild animals experiencing different levels of DEE throughout their annual cycle, the results presented here could explain why the relationships between BMR and other components of the energy budget (e.g., activity, growth, reproduction) are often context dependent.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".