Data from: The pyriform egg of the Common Murre (Uria aalge) is more stable on sloping surfaces
Bibliographic record
Abstract
The adaptive significance of avian egg shape is a long-standing problem in biology. For many years, it was widely believed that the pyriform shape of the Common Murre (Uria aalge) egg allowed it to either “spin like a top” or “roll in an arc,” thereby reducing its risk of rolling off the breeding ledge. There is no evidence in support of either mechanism. Two recent alternative hypotheses suggest that a pyriform egg confers mechanical strength and minimizes the risk of dirt contamination of the blunt end. We present a new hypothesis: that the Common Murre egg's pyriform shape confers stability on the breeding ledge, thus reducing the chance that it will begin to roll. We tested this hypothesis by measuring the stability of Common Murre and Razorbill (Alca torda) eggs of different shapes on slopes of 20°, 30°, and 40° above the horizontal. Common Murre eggs were more stable, and easier to stabilize, than the more elliptical Razorbill eggs. Within Common Murre eggs, more pyriform eggs were more stable. From a fitness perspective, the stability of the Common Murre egg on a slope seems likely to confer an advantage and thus may be a strong force of natural selection favoring the pyriform shape.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.038 | 0.008 |
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".