Trade‐offs between egg size and number in waterfowl: an interspecific test of the van Noordwijk and de Jong model
Bibliographic record
Abstract
Abstract 1. Van Noordwijk & de Jong proposed a model to predict the sign of an intraspecific correlation between two life‐history traits, given that a trade‐off between the two traits exists. In this paper, the model is adapted to egg size/number trade‐offs. 2. The predictions of the model are tested within various clades of waterfowl. Although the relationship between egg mass and clutch size in this group has been examined previously, prior analyses have either failed to take phylogenetic structure into account or relied upon phylogenies that lack resolution. Phylogenetic structure is adjusted for using recently constructed phylogenies and the method of phylogenetically independent contrasts. 3. Van Noordwijk and de Jong's model was successful in explaining between‐clade variation in the relationship between egg mass and clutch size. Even if a trade‐off between egg size and number exists, a negative relationship will only be observed if the variation in the pattern of allocation (i.e. few large eggs vs many small eggs) is high relative to the variation in the amount of resources invested in the clutch. Thus, this model may be useful for understanding interspecific relationships as well as intraspecific patterns.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".