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Record W4408031029 · doi:10.1139/as-2024-0055

Summer foraging habitat suitability for highly mobile male beluga whales in the Eastern Beaufort Sea and Arctic Archipelago

2025· article· en· W4408031029 on OpenAlexafffundvenue
Luke Storrie, Nigel E. Hussey, Shannon A. MacPhee, Gregory O’Corry-Crowe, Lisa L. Loseto

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaUniversity of WindsorUniversity of Manitoba
FundersFisheries and Oceans CanadaNatural Resources CanadaCrown-Indigenous Relations and Northern Affairs CanadaUniversity of ManitobaArcticNetFisheries Joint Management Committee
KeywordsBeaufort seaBeluga WhaleArchipelagoArcticBeaufort scaleForagingFisheryHabitatOceanographyBelugaGeographyEnvironmental scienceEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Eastern Beaufort Sea belugas face threats from increasing vessel traffic and decreasing availability of Arctic cod driven by climate change. This necessitates an improved understanding of the environmental drivers of their foraging habitat suitability to make predictions on the consequences of climate change and to inform management decisions. We incorporated animal behaviour into habitat suitability models through comparing the locations of dives associated with foraging with randomly selected locations to quantify summer habitat preferences. We tested whether environmental drivers associated with foraging dives varied between months (July/August) and years (2018/2019). Seafloor depth (350–750 m) was the most important variable determining foraging habitat suitability and was consistent between years. Studies on beluga diet alongside these results suggest that foraging habitat suitability is primarily driven by the presence of Arctic cod. Belugas consistently targeted bathymetric regimes irrespective of ice conditions, suggesting that sites in the Beaufort Sea and Arctic Archipelago may maintain suitable foraging habitat under near-future changes in sea ice. As vessel traffic is predicted to increase in these regions as seasonal sea ice declines, these results can be incorporated into management decisions to identify areas of conservation interest to mitigate possible acoustic threats to beluga whales.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.997

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.022
GPT teacher head0.280
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

Citations1
Published2025
Admission routes3
Has abstractyes

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