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Record W2345461517 · doi:10.3354/meps11424

Dietary tracers in Bathyarca glacialis from contrasting trophic regions in the Canadian Arctic

2015· article· en· W2345461517 on OpenAlexafffundabout
B. Gaillard, Tarik Meziane, Réjean Tremblay, Philippe Archambault, AL Martel, Frédéric Olivier

Bibliographic record

VenueMarine Ecology Progress Series · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsCanadian Museum of NatureUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaMuséum National d'Histoire NaturelleOntario Genomics InstituteGovernment of CanadaArcticNetGenome CanadaOntario GenomicsUniversité du Québec à Rimouski
KeywordsTrophic levelArcticOceanographyEnvironmental scienceEcologyGeographyThe arcticFisheryBiologyGeology

Abstract

fetched live from OpenAlex

This study used fatty acid trophic markers (FATMs) to assess carbon sources of the bivalve Bathyarca glacialis and describe the pelagic-benthic coupling in the Canadian Arctic Archipelago. Four regions characterized by contrasting trophic environments were investigated: Southeastern Beaufort Sea, Victoria Strait, Lancaster Sound and Northern Baffin Bay. Our results suggest that B. glacialis is a non-selective filter feeder, feeding on microalgae, zooplankton, and bacteria. Diet was based mainly on microalgae, especially for coastal populations of the Southeastern Beaufort Sea. However, zooplankton and bacteria contributed more significantly to the diet of B. glacialis in bathyal populations than the coastal populations. Local and seasonal environmental conditions likely explain these differences in diet between populations. Furthermore, nonmethylene-interrupted (NMI) fatty acids were present in the polar lipids of B. glacialis, which could be produced de novo when access to essential fatty acids (EFAs), required for maintaining membrane structure and function, is limited. This physiological response could help the bivalve to modulate its membrane fluidity in the face of constraints of the deep-sea environment such as low temperatures, high pressure, and when EFAs are less available in its diet. This bivalve species thus has certain attributes that could help it to cope with expected strong modifications in primary production dynamics due to climate-induced changes in the Arctic marine system.

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 score0.671
Threshold uncertainty score0.998

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)0.0010.000

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 head0.020
GPT teacher head0.237
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.

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 routes3
Has abstractyes

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