Sons accelerate maternal aging in a wild mammal
Bibliographic record
Abstract
Aging, or senescence, is a progressive deterioration of physiological function with age. It leads to age-related declines in reproduction (reproductive senescence) and survival (actuarial senescence) in most organisms. However, senescence patterns can be highly variable across species, populations, and individuals, and the reasons for such variations remain poorly understood. Evolutionary theories predict that increases in reproductive effort in early life should be associated with accelerated senescence, but empirical tests have yielded mixed results. Although in sexually size-dimorphic species offspring of the larger sex (typically males) commonly require more parental resources, these sex differences are not currently incorporated into evolutionary theories of aging. Here, we show that female reproductive senescence varies with both the number and sex ratio of offspring weaned during early life, using data from a long-term study of bighorn sheep. For a given number of offspring, females that weaned more sons than daughters when aged between 2 and 7 y experienced faster senescence in offspring survival in old age. By contrast, analyses of actuarial senescence showed no cost of early-life reproduction. Our results unite two important topics in evolutionary biology: life history and sex allocation. Offspring sex ratio may help explain among-individual variation in senescence rates in other species, including humans.
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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 | 0.000 | 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".