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Record W2170620514 · doi:10.1139/f05-219

Variable uptake and elimination of stable nitrogen isotopes between tissues in fish

2006· article· en· W2170620514 on OpenAlexvenueno aff
M. Aaron MacNeil, Ken G. Drouillard, Aaron T. Fisk

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelTurnoverMetabolismBiologyOmnivoreEarthwormAnimal scienceFreshwater fishEcologyFish <Actinopterygii>BiochemistryFisheryPredation

Abstract

fetched live from OpenAlex

We conducted a diet-switching experiment using freshwater ocellate river stingrays (Potamotrygon motoro) fed a novel earthworm (Eisenia foetida) diet to establish the relative contributions of growth and metabolism to δ 15 N values in an elasmobranch species. We specifically controlled for the potential effects of protein composition of experimental diets on δ 15 N turnover to determine whether δ 15 N turnover after a low to high δ 15 N diet switch (uptake) and a high to low δ 15 N diet switch (elimination) will occur at the same rate within each consumer tissue. Our results showed that the turnover of δ 15 N from metabolism and growth differed between uptake and elimination phases in the liver, blood, cartilage, and muscle of freshwater stingrays. During uptake, liver was found to track dietary δ 15 N more closely than the other tissues, with the highest metabolic turnover rate of δ 15 N (0.015 day –1 ), whereas cartilage had the slowest rate of metabolic δ 15 N turnover (0.0022 day –1 ) relative to a constant rate of growth among tissues (0.003 day –1 ). We propose that estimates of trophic position from muscle sampling alone have considerable uncertainty, particularly for scavenging or omnivorous species. We suggest that multitissue sampling can identify this problem and lead to a more robust evaluation of trophic dynamics for individual species.

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 score0.127
Threshold uncertainty score0.964

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 studies0.0000.001
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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations229
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicIsotope Analysis in EcologyFrench-language works237,207