Corticosterone in feathers is a long‐term, integrated measure of avian stress physiology
Bibliographic record
Abstract
1 Stress has pervasive consequences for the well-being of animals. Currently, understanding how individuals cope with stressors is typically accomplished via short-term quantification of blood glucocorticoids released after activation of the hypothalamic–pituitary–adrenal (HPA) axis. 2 We investigated whether the amount of corticosterone (CORT) deposited in growing feathers provides a long-term, integrated measure of HPA activity in birds using captive red-legged partridges Alectoris rufa as a model species. 3 We examined CORT levels in primary feathers induced to grow at the same time as stress series were performed with a capture and restraint protocol. Plasma CORT titres after stress-induced stimulation, but not baseline values, correlated with feather CORT. Feather levels showed the same pattern as plasma of decline across the breeding season, but more severely. 4 For females, CORT in naturally moulted flank feathers was highly and positively correlated with the number of eggs laid in the previous few months, but not clutch size of the following year. For males, the amount of black on a feather, known to be a social signal, was positively correlated with its CORT level. 5 The analysis of feather CORT is a novel methodology that allows for meaningful interpretations of how individuals respond to environmental perturbations and adjust to life-history stages. 6 The analysis of feather hormones has the unique advantages of allowing for experimentation and sampling at any time of the year with minimal investigator-induced impacts and artefacts, and shows the HPA activity of an individual with a flexible time frame from days to months depending on the length of time taken to grow the feather. As this technique can be applied to living or dead birds, or feathers picked up after moult, it provides the ultimate non-invasive physiological measure of considerable benefit in terms of animal welfare and sampling effort.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".