Adaptive allocation of stress-induced deformities on bird feathers
Bibliographic record
Abstract
Physiological stress during ontogeny is known to cause abnormalities in keratin structures of vertebrates, but little is known about if and how organisms have evolved mechanisms to reduce the negative effects of these abnormalities. Stress experienced during avian feather growth is known to lead to the formation of fault bars, and thereby to the weakening of feathers because of shortage and slimming of barbules. Here we propose and test a new hypothesis (the 'fault bar allocation hypothesis') according to which birds should have evolved adaptive strategies to counteract this evolutionary pressure. In particular, we predicted and tested the idea that in flying birds, natural selection should have selected for mechanisms to reduce fault bar load on feathers with high strength requirements during flight. Data on the growth of feathers of nestling white storks (Ciconia ciconia) revealed a consistent allocation of more, and more intense, fault bars in innermost than in outermost wing feathers as predicted by our hypothesis. Moreover, the same pattern emerged from feathers of adult storks. We discuss the generality of our results, and suggest avenues for further investigations in this area.
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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 | 0.000 | 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".