The shape of eggs in different-sized clutches of the house wren (<i>Troglodytes aedon</i>)
Bibliographic record
Abstract
The substantial inter- and intra-specific variation that occurs in the shape of birds' eggs remains largely unexplained. Several researchers have hypothesized that selection will favour a shape that results in the most compact fit of eggs under the incubating parent. Mathematical modeling has suggested that optimal egg shape depends on the number of eggs laid. We tested predictions of one previously published model by comparing the shape of eggs in clutches of 4, 5, 6, 7, and 8 eggs in a Wyoming, U.S.A., population of the house wren (Troglodytes aedon), a small, cavity-nesting songbird whose eggs vary considerably in shape. Contrary to predictions of the model, the typical shapes of eggs from different-sized clutches were very similar. Moreover, slight differences in shape detected were not in directions predicted by the model. There may be less selection on house wrens to adjust egg shape to clutch size because they lay eggs that are small relative to female body size, because eggs are placed in a deep spherical cup that conforms to the shape of the female's abdomen and because nests are to some degree insulated, being located in tree cavities. Full evaluation of the model will require comparisons of egg shape and clutch size in a large set of species that differ in natural-history characteristics such as egg size, nest shape, and nest placement.
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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 | 0.001 | 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".