Revisiting the positive correlation between female size and egg size
Bibliographic record
Abstract
Positive correlations between maternal size and propagule (egg, seed, embryo) size could arise for several reasons. One of these is that larger mothers may improve the survival of their offspring during a stage when large propagules typically have lower survival than small propagules. We previously developed an optimality model that incorporated this effect and used it to explain the positive correlation between female size and egg size in some fishes. Our original analysis included the common assumption that large eggs have lower survival than small eggs when dissolved oxygen is low (because of surface-to-volume constraints). Recent empirical work, however, has suggested just the opposite: large eggs actually have higher survival than small eggs when dissolved oxygen is low. Here we re-analyse our original model in the light of this new evidence, showing that the original explanation for positive egg size–female size correlations no longer holds, but that new candidate explanations emerge. Specifically, larger females should produce larger eggs when they provide incubation environments of lower quality (i.e. lower dissolved oxygen). One way this might occur is that larger females produce larger clutches, which should have higher total oxygen demand. The re-analysis demonstrates that our theoretical approach can accommodate a variety of assumptions and may prove useful as a general framework for predicting variation in optimal egg size.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".