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Record W2603034296 · doi:10.1093/conphys/cox021

Compatibility of preparatory procedures for the analysis of cortisol concentrations and stable isotope (δ13C, δ15N) ratios: a test on brown bear hair

2017· article· en· W2603034296 on OpenAlexaff
Agnieszka Sergiel, Keith A. Hobson, David M. Janz, Marc Cattet, Nuria Selva, Luciene Kapronczai, Chantel Gryba, Andreas Zedrosser

Bibliographic record

VenueConservation Physiology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsSaskatchewan Health AuthorityUniversity of SaskatchewanWestern UniversityEnvironment and Climate Change Canada
FundersNarodowe Centrum Badań i RozwojuNaturvårdsverketAustrian Science FundMiljødirektoratetSvenska Jägareförbundet
KeywordsMethanolδ13CIsotopeChloroformStable isotope ratioδ15NUrsusIsotope analysisBiologyIsotopes of nitrogenEnvironmental chemistryHair analysisChromatographyNitrogenChemistryEcologyOrganic chemistryPopulationPathology

Abstract

fetched live from OpenAlex

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 ( 13 C) and nitrogen ( 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, 13 C and 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.282
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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