MétaCan
Menu
Back to cohort
Record W2105965298 · doi:10.1577/t04-124.1

Stable Isotope Variability in Tissues of Temperate Stream Fishes

2005· article· en· W2105965298 on OpenAlexafffund
Timothy D. Jardine, Michelle A. Gray, Sherisse M. McWilliam, Richard A. Cunjak

Bibliographic record

VenueTransactions of the American Fisheries Society · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of ManitobaUniversity of New Brunswick
FundersParks Canada
KeywordsSculpinSalvelinusFontinalisBiologySalmoGonadCottusTroutZoologyJuvenileStable isotope ratioMuscle tissueNutrientFisheryEcologyFish <Actinopterygii>Anatomy

Abstract

fetched live from OpenAlex

Abstract Previous measurements of stable isotope ratios in fishes have typically used white muscle, but potential applications exist for the use of other tissues. We tested three tissues (liver, fin, and gonad) in three freshwater species (juvenile Atlantic salmon Salmo salar , slimy sculpin Cottus cognatus , and brook trout Salvelinus fontinalis ) to investigate potential ecological applications of stable isotopes in tissues other than muscle. Caudal fin tissue correlated closely with muscle tissue for Atlantic salmon and brook trout for δ 13 C ( r = 0.96 and 0.94, respectively) and δ 15 N ( r = 0.80 and 0.74). Liver δ 13 C values were tightly linked to muscle values, and differences were due to lipid effects. Associations between liver and muscle δ 15 N suggested subtle changes in nutritional status. Isotope ratios of gonads differed markedly between male and female slimy sculpin; these differences were probably governed by differences in the allocation of specific nutrients. Knowledge of isotopic fractionation among tissues will aid fish biologists in nonlethal sampling of fishes for stable isotope analysis and in using stable isotopes to assess nutritional status and the allocation of nutrients to reproduction.

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)0.000
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 score0.065
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations130
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueTransactions of the American Fisheries SocietySame topicIsotope Analysis in EcologyFrench-language works237,207