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Record W2898513720 · doi:10.1111/mms.12546

Examination of relationships between stable isotopes and cortisol concentrations along the length of phocid whiskers

2018· article· en· W2898513720 on OpenAlexaff
Shawna Karpovich, John P. Skinner, Luciene Kapronczai, Justin S. Smith, David M. Janz

Bibliographic record

VenueMarine Mammal Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Saskatchewan
FundersNational Oceanic and Atmospheric AdministrationAlaska Department of Fish and Game
KeywordsPhocaWhiskersHarbor sealWhiskerStable isotope ratioBiologyZoologyAnimal scienceChemistryEcologyMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Alaskan seals are found in remote and sometimes inaccessible locations, making it difficult to collect time‐series information. This study explores a novel method to examine temporal changes in diet and physiological status of ringed ( Pusa hispida ), spotted ( Phoca largha ), and harbor ( Phoca vitulina ) seals using cortisol concentrations and δ 15 N and δ 13 C stable isotopes (SIs) measured in serial sections of whiskers. As whiskers grow, whisker tissue is deposited sequentially making these measurements temporally aligned. Whisker cortisol presented in a distinct pattern with elevated concentrations at the root section followed by a curvilinear decline moving toward the tip of most whiskers. Comparing SIs at the root to the rest of the whiskers, δ 13 C values were slightly lower in ringed and harbor seal whiskers and δ 15 N values were slightly higher in harbor seal whiskers. The data were modeled controlling for the observed trends in cortisol concentrations and further associations between cortisol concentrations and SIs were detected in spotted and harbor seal whiskers. Additional research examining the source and stability of whisker cortisol is warranted. However, the methods presented here demonstrate that whiskers could prove valuable to gather long‐term and naturally aligned dietary and physiological information.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.054
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.244
Teacher spread0.215 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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