Maternal Effects Underlie Ageing Costs of Growth in the Zebra Finch (Taeniopygia guttata)
Bibliographic record
Abstract
Maternal effects provide a mechanism to adapt offspring phenotype and optimize the mother's fitness to current environmental conditions. Transferring steroids to the yolk is one way mothers can translate environmental information into potential adaptive signals for offspring. However, maternally-derived hormones might also have adverse effects for offspring. For example, recent data in zebra finch chicks suggested that ageing related-processes (i.e. oxidative stress and telomere loss) were increased after egg-injection of corticosterone (CORT). Still, we have few experimental data describing the effect of maternal effects on the growth-ageing trade-off in offspring. Here, we chronically treated pre-laying zebra finch females (Taeniopygia guttata) with 17-β-estradiol (E2) or CORT, and followed offspring growth and cellular ageing rates (oxidative stress and telomere loss). CORT treatment decreased growth rate in male chicks and increased rate of telomere loss in mothers and female offspring. E2 increased body mass gain in male offspring, while reducing oxidative stress in both sexes but without affecting telomere loss. Since shorter telomeres were previously found to be a proxy of individual lifespan in zebra finches, maternal effects may, through pleiotropic effects, be important determinants of offspring life-expectancy by modulating ageing rate during embryo and post-natal growth.
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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".