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Record W2040307267 · doi:10.3354/meps10029

Composition and temporal variation in the diet of beluga whales, derived from stable isotopes

2012· article· en· W2040307267 on OpenAlexafffundabout
M Marcoux, BC McMeans, AT Fisk, SH Ferguson

Bibliographic record

VenueMarine Ecology Progress Series · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of WindsorFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBeluga WhaleBelugaδ13CTrophic levelδ15NEcologyBiologyBenthic zoneOtolithPredationGeographyArcticFisheryStable isotope ratioFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The diet of individuals within a species commonly differs among sex and age classes because of differences in energy requirements and physiological needs. Belugas Delphinapterus leucas show a high level of sexual habitat segregation and dimorphism that could result in differences in diet between the sexes. Here, we used stable isotopes of carbon ( 13 C) and nitrogen ( 15 N) from muscle and skin samples of 88 belugas, and likely prey species, to investigate how beluga diet in Cumberland Sound (Nunavut, Canada) varied between sexes, among age classes, and over time from 1982 to 2009. Based on linear mixed-effects models, older belugas had higher 13 C and 15 N than younger individuals of both sexes, suggesting that older individuals feed on more benthic, higher trophic-position prey than younger individuals. We also found a strong, decreasing trend in both 13 C and 15 N values over time, indicating either a temporal shift in beluga diet or an ecosystem-wide change in isotope values. Based on stable isotope mixing models performed on belugas sampled since 2000, both males and females fed primarily on Arctic cod Boreogadus saida and capelin Mallotus villosus. The latter is a recent invader to this ecosystem, which could explain the temporal shift in stable isotopes of the Cumberland Sound belugas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations89
Published2012
Admission routes3
Has abstractyes

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