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Record W2103324874 · doi:10.1111/mms.12212

Age estimation of belugas, <i>Delphinapterus leucas</i>, using fatty acid composition: A promising method

2015· article· en· W2103324874 on OpenAlexafffund
Marianne Marcoux, Véronique Lesage, Gregory W. Thiemann, Sara J. Iverson, Steven H. Ferguson

Bibliographic record

VenueMarine Mammal Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of ManitobaDalhousie UniversityYork UniversityFisheries and Oceans Canada
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaParks CanadaUniversity of Manitoba
KeywordsBlubberLeucasBiologyMarine mammalZoologyBelugaAgeingBeluga WhaleEcologyFishery

Abstract

fetched live from OpenAlex

Abstract Data about age‐specific survival and mortality rate, as well as life history parameters are essential for studying population demography. However, noninvasive methods for ageing free‐ranging marine mammals are generally lacking. Recently, a few studies have highlighted the potential of using fatty acid ( FA ) composition in blubber biopsy samples to estimate age in some cetaceans. Here, we explore the opportunity of using this technique to estimate the age of free‐ranging belugas from three different populations. Belugas ( Delphinapterus leucas ) were sampled postmortem for blubber FA analysis and aged by counting the number of growth layer groups in teeth dentine. We found significant positive and negative relationships between some FA s and age. These relationships were stronger with outer blubber layer samples, the layer most accessible via biopsy, than with inner or middle layer samples, a pattern that is consistent with observed turnover rates and biological function across the blubber depth. The FA 12:0, 14:1n‐7, and 14:1n‐9 were promising correlates of age in belugas, allowing estimation of age with a precision of ±7–10 yr. Further work is required to determine the mechanisms underlying changes in FA composition with age and whether these mechanisms are stable through time and across populations.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.053
GPT teacher head0.312
Teacher spread0.259 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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