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Harvester selection and observed mercury levels in Eastern Beaufort Sea and Western Hudson Bay beluga whales (Delphinapterus leucas)

2024· article· en· W4402557257 on OpenAlexafffundabout
Enooyaq Sudlovenick, Verna Pokiak, Heidi K. Swanson, Jane L. Kirk, Lisa L. Loseto

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change CanadaUniversity of WaterlooUniversity of Manitoba
FundersNorthern Contaminants ProgramFisheries and Oceans CanadaFoundation for the National Institutes of HealthNunavut Wildlife Management BoardW. Garfield Weston FoundationGarfield Weston Foundation
KeywordsLeucasBeluga WhaleBeaufort seaBayMercury (programming language)BelugaFisheryBeaufort scaleCetaceaMarine mammalOceanographyGeographyBiologyArcticGeology

Abstract

fetched live from OpenAlex

Mercury in marine biota has been extensively studied across Inuit Nunaat because it bioaccumulates and biomagnifies in high trophic level species, such as the beluga whale ( Delphinapterus leucas ), or qilalugaq in Inuktut. Qilalugaait (pl) are a staple in many coastal Inuit communities, including Tuktoyaktuk, Northwest Territories and Arviat, Nunavut. We examine how total mercury (THg) concentrations in two beluga populations are influenced by biased sampling resulting from local harvester preferences. We examined historical THg in skin, muscle, and liver (1980's to 2022) together with local qualitative interviews from two beluga-harvesting communities. Age and length bins were used to compare similar sized and aged whales between locations, where males (350 - 400 cm, and 20–30 years) and females (330–400 cm, and 15–30 years) were segregated. The interviews revealed distinct preferences whereby harvesters in Tuktoyaktuk actively sought larger (length) male whales, whereas harvesters in Arviat, selected wide and even range across size and sex. These local preferences were also evident in the historical dataset, with the median age and lengths were 31 years and 389.0 cm in Tuktoyaktuk ( n = 461) and 23 and 336.0 cm in Arviat ( n = 146). For males, mean and median THg concentrations were higher in beluga harvested from Tuktoyaktuk than Arviat in all three tissues with age and lengths combined, yet in the selected age and length bins, there was no difference in mean and median THg in the muscle tissue, and in median liver THg. There were significant differences in mean and median skin THg and in mean liver THg concentrations between males. In female whales, THg concentrations did not differ between Tuktoyaktuk and Arviat (in ages and lengths combined and in selected age bins across all tissues), excluding median muscle THg concentration. This study indicated that differences in THg concentrations that were previously observed resulted from hunter preferences in these two communities. • Mercury concentrations in two beluga populations were examined together with harvester interviews. • Both mercury dataset and interviews show clear harvest preferences between the two communities (size and sex difference). • Mercury concentrations are more similar between the Eastern Beaufort Sea and Western Hudson Bay than previously thought. • Local contexts and cultural difference should be considered as they may impact data interpretation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.166
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
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 head0.036
GPT teacher head0.241
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2024
Admission routes3
Has abstractyes

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