Context-dependent correlation between resting metabolic rate and daily energy expenditure in wild chipmunks
Bibliographic record
Abstract
Several empirical studies have shown that variation in daily energy expenditure (DEE) and resting metabolic rate (RMR) is influenced by environmental and individual factors, but whether these shared influences are responsible for, or independent of, relationships between DEE and RMR remains unknown. The objectives of this study were to (i) simultaneously evaluate the effects of environmental and individual variables on DEE and RMR in free-ranging eastern chipmunks (Tamias striatus) and (ii) quantify the correlation between DEE and RMR before and after controlling for common sources of variation. We found that the influence of individual factors on DEE and RMR is most often shared, whereas the influence of environmental factors tends to be distinct. Both raw and mass-adjusted DEE and RMR were significantly correlated, but this correlation vanished after accounting for the shared effect of reproduction on both traits. However, within reproductive individuals, DEE and RMR remained positively correlated after accounting for all other significant covariates. The ratio of DEE to RMR was significantly higher during reproduction than at other times of the year and was negatively correlated with ambient temperature. DEE and RMR appear to be inherently correlated during reproduction, but this correlation does not persist during other, less energy-demanding periods of the annual cycle.
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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 | 0.001 | 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".