Hair cortisol concentration as a noninvasive measure of long-term stress in free-ranging grizzly bears (Ursus arctos): considerations with implications for other wildlife
Bibliographic record
Abstract
Human-caused landscape change negatively affects the sustainability of many wildlife populations. In Alberta, Canada, grizzly bears ( Ursus arctos L., 1758) live in one of the most populated and heavily exploited landscapes in which the species survives. Long-term physiological stress in individual animals may be the predominant mechanism linking landscape change with impaired wildlife population health. Hair cortisol concentration has been validated as a biomarker of long-term stress in humans and domestic animals; however, limited work has examined factors that may affect its measurement or interpretation. We have measured cortisol in as few as five guard hairs of a grizzly bear and have identified factors influencing hair cortisol concentration in this species. Hair cortisol varies with hair type, body region, and capture method. It is not influenced by colour, age, sex, environmental exposure (18 days), or prolonged laboratory storage (>1 year) and does not vary along the length of the hair shaft. Recommendations for prudent use of hair cortisol analysis in grizzly bears are discussed with implications for the development of hair cortisol concentration as a tool to monitor long-term stress in other wildlife.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".