Exceptional variation in the appearance of Common Murre eggs reveals their potential as identity signals
Bibliographic record
Abstract
Abstract We studied the ground colors and maculations of 161 Common Murre (Uria aalge) eggs laid by 43 females in 3 small breeding groups on the cliffs of Skomer Island, Wales, in 2016–2018. Both the colors and maculations varied much more among than within females, providing quantitative evidence for the egg traits that might facilitate the parents’ ability to identify their own eggs on the crowded breeding ledges where the density is typically ~20 eggs m–2. Ground colors had a trimodal distribution of hue values (whitish to pale brown, pale blue, or vivid blue-green) and maculations ranged from none to complex squiggles and blotches. The eggs laid by each female in different years were similar to one another, and replacement eggs laid by females within years were also more similar to their first egg than to other eggs in the same breeding group. Egg appearance did not differ among the 3 breeding groups that we studied. Our findings thus support anecdotal observations that, within and between years, female Common Murres lay eggs that have similar ground colors and maculations. We do not, however, find evidence that there is much difference among the eggs laid in different parts of a colony.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".