Stress during feather development predicts fitness potential
Bibliographic record
Abstract
Summary Measures of the quality of an individual are important in the study of proximate and ultimate factors in biology. Records of developmental history are particularly desirable, as many phenotypical traits are influenced by conditions during growth. Conspicuous irregularities in feathers, known as fault bars, result from a variety of stresses that occur during feather growth. The frequency of fault bars was evaluated on primary and tail feathers (grown 1 year previously) of 1919 adult American kestrels from a breeding population in Canada (1990–97). Most (91·5%) birds exhibited some fault‐barring, although females had significantly more feathers with fault bars than males (17% vs. 14%, respectively). Body size, intensity of haematozoan infections and leucocyte differentials were all unrelated to fault bars; however, birds with many fault bars were in poor body condition during prelaying (males) and incubation (males and females). Individual kestrels tended to be consistent in the number of feathers with fault bars from year to year. The percentage of feathers with fault bars was not associated with the timing of arrival in spring or prey abundance per territory; however, birds of both sexes with many bars were less likely to breed. Birds paired non‐randomly, as mates tended to have a similar frequency of fault bars. Males and females with many bars had significantly later clutch initiation dates, but there were no negative consequences regarding clutch size or egg size. Female kestrels with many fault bars had lower survival probabilities. Both sexes were also less likely to be recaptured in years following initial banding if they had many bars, suggesting that they were more likely to emigrate from the study area temporarily. Fault bars on feathers appear to be indicative of an individual’s susceptibility to stress, and are useful in predicting components of fitness. The use of fault bars is a promising tool as they are easy to evaluate and can be assessed on live or dead birds, on moulted feathers and on individuals repeatedly over time.
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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) | 0.006 | 0.001 |
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".