Compatibility of preparatory procedures for the analysis of cortisol concentrations and stable isotope (δ13C, δ15N) ratios: a test on brown bear hair
Bibliographic record
Abstract
The measurement of naturally occurring glucocorticoids and stable isotopes of several elements has gained importance in wildlife studies in recent decades and opened a myriad of ecological applications.Cortisol and stable isotopes equilibrate in animal tissues over periods of integration related to the growth rate of the tissue, providing information reflecting systemic cortisol secretion and dietary intake.Sample preparation shares the common step of first cleaning the sample of external contamination.However, it is not well understood how different solvents used in sample preparation affect isotopic and cortisol values, and whether it is safe to follow the same procedures for both measures to optimize analyses of the same sample.We conducted an experiment to compare different preparation protocols for the analysis of cortisol concentrations and stable carbon (δ 13 C) and nitrogen (δ 15 N) isotope ratios in hair.Hair samples from 12 brown bears (Ursus arctos) were each divided into five aliquots; two aliquots were rinsed with a 2:1 chloroform:methanol (v/v) mixture with one aliquot ground prior to cortisol analysis and the other left intact for stable isotope analyses; two aliquots were washed with methanol with one aliquot ground prior to cortisol analysis and the other left intact for stable isotope analyses; and one aliquot washed with methanol and ground prior to stable isotope analyses.The cortisol, δ 13 C and δ 15 N values remained consistent following all treatments.Our results indicate that hair samples rinsed with a 2:1 chloroform:methanol mixture or washed with methanol can be used for both types of analyses.Further, hair that has been ground in a standard hair cortisol procedure can also be used for stable isotope analysis.This information is important for improving laboratory efficiency and compatibility of procedures used for wildlife physiological ecology studies where concurrent measurements of cortisol and stable isotopes in hair are required.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".