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Record W4377087150 · doi:10.1139/as-2021-0026

Beluga Whale Body Condition: Evaluating environmental variables on beluga body condition indicators in the Tarium Niryutait MPA, Beaufort Sea.

2023· article· en· W4377087150 on OpenAlexafffundvenue
Kate McMillian, Carie Hoover, John Iacozza, Jonathan Peyton, Lisa L. Loseto

Bibliographic record

VenueArctic Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaDalhousie UniversityUniversity of Manitoba
FundersFisheries and Oceans CanadaArcticNetFisheries Joint Management Committee
KeywordsBeluga WhaleBlubberBelugaFisheryLeucasCondition indexGeographyEnvironmental scienceOceanographyBiologyEcologyArcticGeology

Abstract

fetched live from OpenAlex

The development of indicators as tools for ecosystem monitoring is a key step in the management of Marine Protected Areas (MPAs). This study uses previously developed sex-specific body condition indices, blubber thickness and girth, to assess temporal changes in body condition from 2000 to 2015 in harvested Eastern Beaufort Sea (EBS) beluga whales (Delphinapterus leucas). Specifically, the goals were to (1) examine seasonal and inter-annual trends of beluga body condition indicators over the harvest season; (2) evaluate associations of body condition indicators across sexes; and (3) test annual means of each body condition index for correlations to regional scale environmental drivers, (i.e. the Pacific Decadal Oscillation (PDO) and sea-ice minimum (SIM) in the Beaufort Sea). Significant seasonal changes in male blubber thickness and female girth indices demonstrated the importance of short term seasonal drivers. Whilst inter-annual changes in girth and blubber thickness indices revealed longer-term changes, that were correlated between males and females. Only the male girth index had significant relationships with environmental drivers: a negative relationship with the PDO at a zero-year lag ,and a negative relationship with the SIM at a two-year lag. [more in manuscript]

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.296
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2023
Admission routes3
Has abstractyes

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