Hair cortisol and stable carbon and nitrogen isotope ratios in barbary macaques ( <scp> <i>Macaca sylvanus</i> </scp> ) from Gibraltar
Bibliographic record
Abstract
Rationale Prolonged exposure to the stress hormone cortisol can precipitate a catabolic state in animals resulting in a negative nitrogen balance and increased excreted nitrogen, possibly affecting stable nitrogen and carbon isotope ratios in animal tissues. Here we test the hypothesis that stress, as measured by hair cortisol, is a significant contributor to variation in δ 15 N and δ 13 C values in hair. The results of the present study have important implications for the use of stable carbon and nitrogen isotope ratios measured in animal and human tissues to make inferences regarding feeding ecology and diet. Methods Samples containing roughly 10–20 hairs (approx. 4–6 cm long) were collected from 49 free ranging macaque monkeys ( Macaca sylvanus ) in Gibraltar. Hair samples were analyzed for stable carbon and nitrogen isotopes by continuous‐flow isotope‐ratio mass spectrometry. Cortisol extracted from matched hair samples was measured using gas chromatography/mass spectrometry. Cortisol and stable isotope ratios were compared statistically using nonparametric Kruskal–Wallis and Mann–Whitney U tests, as well as Spearman correlation. Results Correlation analysis revealed that although hair δ 15 N and δ 13 C values were significantly correlated with each other, neither was correlated with cortisol concentrations. Cortisol concentrations were not correlated with age‐adjusted body mass index. Age category was correlated with δ 13 C values but not with δ 15 N values. Conclusions The results of our study suggest that the cumulative stress experienced by these macaques under typical social and environmental conditions is not a significant contributor to variation in δ 15 N and δ 13 C values, nor does it have a significant effect on relative body mass.
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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 | 0.001 | 0.001 |
| 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".