Experimental manipulation of female reproduction reveals an intraspecific egg size clutch size trade-off
Bibliographic record
Abstract
A negative relationship, or trade-off, between egg size and clutch size is a central and long-standing component of life-history theory, yet there is little empirical evidence for such a trade-off, especially at the intraspecific level. Here, I show that female zebra finches (Taeniopygia guttata) treated chronically during egg formation with the anti-oestrogen tamoxifen lay smaller eggs (by 8%) but produce larger clutches (on average two eggs more) than controls. Decreased egg mass in tamoxifen-treated females was associated with a 50% decrease in plasma levels of the two yolk precursors, vitellogenin and very-low-density lipoprotein. Although tamoxifen-treated females laid more, smaller eggs (and had a higher total expenditure in their clutch), they did not differ from controls in the number of chicks fledged, the mass or size of these chicks at fledging, or the chicks' egg-production performance at three months of age. However, tamoxifen-treated females had lower relative hatching success: they laid more eggs but hatched the same number of chicks. Among individual tamoxifen-treated females, birds that laid the smallest eggs early in their laying sequence laid the largest number of additional eggs, that is, there was a negative correlation, or trade-off, between egg size and clutch size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".