The effects of body region, season and external arsenic application on hair cortisol concentration
Bibliographic record
Abstract
Hair cortisol analysis has been used to quantify hormone levels in circulation in several mammal species. Hair remains stable for decades or centuries, allowing researchers to use archived hair samples to investigate hormone levels that span long time periods. However, several studies have found that intra-individual variability, driven by the body region from which a sample is derived, confounds measurements of systemic glucocorticoid hormone concentrations. In addition, the external application of chemical agents to hair can remove or concentrate molecules of interest. These may preclude the use of samples that have been collected opportunistically and/or those that have been housed in museum collections. Using a captive population of Vancouver Island marmots (Marmota vancouverensis), we found a strong effect of body region on the concentration of cortisol within hair, as well as an effect of season. Using a collection of American mink (Neovison vison) pelts, we found that application of the preservative arsenic in the form of a soap does not cause a significant decrease in cortisol. The marmot results suggest that intra-individual variability is not stable through time. The reason for these seasonal effects is not clear and further study is necessary. Researchers using samples from an unknown body region should exercise caution in interpreting their results. The mink results suggest that samples held in museum collections can be used to quantify cortisol, even when arsenic preservation is suspected.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".