Multicomponent shell traits are consistent with an individual recognition function of the appearance of common murre (<i>Uria aalge</i>) eggs: A biological replication study
Bibliographic record
Abstract
) are still able to identify their own eggs. Each female murre's egg is thought to be recognized individually by the shell's avian-perceivable traits. This is because the eggshells' visible traits conform to expectations of the identity-signaling hypothesis in that they show both high intraindividual repeatability and high interindividual variability. Identity signaling also predicts a lack of correlation between each of the putative multicomponent recognition traits, yielding no significant relationships between those eggshell traits that are generated by mutually exclusive physiological factors. Using a multivariate analysis across eggshell size and shape, avian-perceivable background coloration, spot (maculation) shape, and spot density, we detected no unexpected statistical correlations between Icelandic common murre egg traits lacking known physiological or mathematical relationships with one another. These results biologically replicate the conclusions of a recent eggshell trait study of Canadian common murres using similar methodology. We also demonstrate the use of static correlations to infer identity signaling function without direct behavioral observations, which in turn may also be applied to rare or extinct species and provide valuable insight into otherwise unknown communicative and behavioral functions.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".