Data from: The active mouse rests within: Energy management among and within individuals
Bibliographic record
Abstract
1. The relationship between daily energy expenditure (DEE) and resting metabolic rate (RMR) provides insight into how organisms allocate energy to maintenance versus energetically expensive activities such as locomotor activity. 2. Three models have been devised to describe energy management: the allocation, independent, and performance models, which respectively predict a DEE-RMR slope of b<1, b=1, and b>1. 3. Here, we took paired repeated metabolic and behavioural measurements in 51 female white-footed mice to 1) evaluate which energy management models apply at the among- and within-individual levels, and to 2) quantify the relationship between metabolic traits and two energetically expensive behaviours. 4. The DEE-RMR slope was different at the among- versus within-individual levels, with values supporting the performance and allocation models at the among- and within-individual levels, respectively. Accordingly, the relationship between voluntary wheel running and RMR was positive at the among-individual level (r = 0.40±0.21), but negative at the within-individual level (r = -0.23±0.10). 5. To our knowledge, this is the first study to simultaneously partition the relationship between RMR and behaviour at the among- versus within-individuals levels while determining which energy management models apply at each of these levels. In doing so, we have identified a mechanism through which compensation occurs at the within-individual level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".