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Record W2520366032 · doi:10.1139/cjfas-2015-0584

Interactive effects of urea and lipid content confound stable isotope analysis in elasmobranch fishes

2016· article· en· W2520366032 on OpenAlexaffvenue
Aaron B. Carlisle, Steven Y. Litvin, Daniel J. Madigan, Kady Lyons, Jennifer S. Bigman, Melissa Ibarra, Joseph J. Bizzarro

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsSimon Fraser UniversityUniversity of Calgary
FundersNational Oceanic and Atmospheric AdministrationNorthwestern University
KeywordsUreaStable isotope ratioBiologyIsotope analysisChemistryEcologyAnimal scienceBiochemistry

Abstract

fetched live from OpenAlex

Stable isotope analysis (SIA) is becoming a commonly used tool to study the ecology of elasmobranchs. However, the retention of urea by elasmobranchs for osmoregulatory purposes may bias the analysis and interpretation of SIA data. We examined the effects of removing urea and lipid on the stable isotope composition of 14 species of sharks, skates, and rays from the eastern North Pacific Ocean. While effects were variable across taxa, removal of urea generally increased δ15N and C:N. Urea removal had less influence on δ13C, whereas extracting urea and lipid generally increased δ15N, C:N, and δ13C. Because C:N values of nonextracted tissues are often used to infer lipid content and adjust δ13C, shifts in C:N following urea extraction will change the inferred lipid content and bias any mathematical adjustment of δ13C. These results highlight the importance of urea and lipid extraction and demonstrate the confounding effects of these compounds, making it impossible to use C:N of non-urea-extracted samples as a diagnostic tool to estimate and correct for lipid content in elasmobranch tissues.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2016
Admission routes2
Has abstractyes

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