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Record W2135104266 · doi:10.1139/z06-136

Contribution of catabolic tissue replacement to the turnover of stable isotopes in Danio rerio

2006· article· en· W2135104266 on OpenAlexvenueno aff
R. Tarboush, Stephen E. MacAvoy, Stephen A. Macko, Victoria Connaughton

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsDanioTrophic levelBiologyIsotopic signatureStable isotope ratioOrganismIsotope analysisIsotopeδ13CSulfurIsotopes of nitrogenCatabolismδ15NEnvironmental chemistryFood chainEcologyZoologyZebrafishBiochemistryMetabolismChemistry

Abstract

fetched live from OpenAlex

Stable isotopes of carbon, nitrogen, and sulfur in organisms can trace nutrient sources and determine trophic interactions. However, time is required for an organism to reflect the isotopic composition of its diet. The rate at which an organism incorporates the isotopic signature of its food has a growth (k) component and a metabolic tissue replacement (m) component. This study shows that metabolic tissue replacement accounts for between 68% and 80% of the observed changes in isotopic signature in zebra danio ( Danio rerio (Hamilton, 1822)) muscle following a dietary shift, with the remainder owing to growth of new tissue. The half-lives of carbon, nitrogen, and sulfur were ~53, ~147 and ~58 days, respectively. This indicates that a significant period of isotopic disequilibria exists before zebra danio resemble dietary isotopic composition. These results show that catabolic replacement of tissue, as well as growth, must be taken into account when predicting the rate of isotope incorporation in warm-water fish. When using isotopic ratios to decipher food webs and trophic interactions, researchers should be aware that the often held assumption of isotopic equilibrium between diet and consumer may be violated.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.209
Teacher spread0.205 · 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.

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

Citations43
Published2006
Admission routes1
Has abstractyes

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