The energetic and oxidative costs of reproduction in a free-ranging rodent
Bibliographic record
Abstract
1. As understanding of the energetic costs of reproduction in birds and mammals continues to improve, oxidative stress is an increasingly cited example of a non-energetic cost of reproduction that may serve as a proximal physiological link underlying life-history trade-offs. 2. Here, we provide the first study to measure daily energy expenditure (DEE) and oxidative damage in a wild population. We measured both traits on eastern chipmunks (Tamias striatus) and assessed their relationships with age, reproductive status, litter size and environmental conditions. 3. We found that both physiological traits were correlated with environmental characteristics (e.g. temperature, seasons). DEE tended to increase with decreasing temperature, while oxidative damage was lower in spring, after a winter of torpor expression, than in autumn. We also found that DEE decreased with age, while oxidative damage was elevated in young individuals, reduced in animals of intermediate age and tended to increase at older age. 4. After controlling for age and environmental variables, we found that both female DEE and oxidative damage increased with litter size, although the latter increased weakly. 5. Our results corroborate findings from laboratory studies but highlight the importance of considering environmental conditions, age and reproductive status in broader analyses of the causes and consequences of physiological costs of reproduction in wild animals.
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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".