Fault bars and the risk of feather damage in cranes
Bibliographic record
Abstract
Fault bars are translucent areas across feathers grown under stressful conditions. They are ubiquitous across avian species and feather tracts. Because fault bars weaken feather structure and can lead to feather breakage, they may reduce flight performance and lower fitness. Therefore, natural selection might prime mechanisms aimed at reducing the cost of fault bars, penalizing their occurrence in those feathers more relevant for flight. Here, we tested one prediction of this ‘fault bar allocation hypothesis’: that the prevalence, abundance and risk of damage of fault bars change across the wing feathers of a long-distance migrant, the sandhill crane Grus canadensis as a function of the strength requirements of feathers for flight. We analysed 2411 wing feathers with 4676 fault bars from 39 cranes in active migration. Fault bars did not increase feather damage with feather age. The occurrence of fault bars decreased from proximal to distal wing portions, both in flight feathers and in coverts, according to the presumed greater strength requirements of external wing feathers during flight. The occurrence of fault bars was variable when producing low feather damage (<2%) but was consistently low for fault bars with a higher damage probability (>2–30%). Altogether, our results suggest that fault bars are common on the feathers of birds even after millions of years of evolution because natural selection seems to penalize birds with particularly harmful fault bars in certain feathers and of a certain magnitude, but is unable to eliminate less harmful fault bars according to their strength and position.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".