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Ultraviolet absorbents and industrial antioxidants in seabirds, mammals, and fish from the Canadian Arctic

2024· article· en· W4401801946 on OpenAlexafffundabout
Ingrid-Alejandra Granados-Galvan, Jennifer F. Provencher, Mark L. Mallory, Amila O. De Silva, Derek C. G. Muir, Jane L. Kirk, Xiaowa Wang, Robert J. Letcher, Lisa L. Loseto, Bonnie M. Hamilton, Zhe Lu

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of TorontoAcadia UniversityFisheries and Oceans CanadaCarleton UniversityEnvironment and Climate Change CanadaUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute for Health and Care ResearchFisheries and Oceans CanadaFonds de recherche du Québec – Nature et technologiesCrown-Indigenous Relations and Northern Affairs CanadaCanadian Celiac Association
KeywordsFish <Actinopterygii>ArcticUltraviolet radiationUltravioletFisheryZoologyThe arcticEnvironmental scienceBiologyEcologyChemistryOceanographyPhysicsOptics

Abstract

fetched live from OpenAlex

Ultraviolet (UV) absorbents and industrial antioxidants are two groups of plastic-derived contaminants of emerging environmental concern. However, their distribution and fate are poorly understood in Arctic wildlife. In the present study, 16 UV absorbents (10 benzotriazole UV stabilizers (BZT-UVs) and 6 organic UV filters (UVFs)) and 7 industrial antioxidants (6 aromatic secondary amines (Ar-SAs) and 2,6-di-tert-butylphenol (26DTBP)) were analyzed in the livers of thick-billed murre (Uria lomvia; n = 28), northern fulmar (Fulmarus glacialis; n = 4), black guillemot (Cepphus grylle; n = 11), polar bear (Ursus maritimus; n = 18), beluga whale (Delphinapterus leucas; n = 10), landlocked (n = 25) and sea-run (n = 10) Arctic char (Salvelinus alpinus) from the Canadian Arctic collected between 2017 and 2021. Compared to industrial antioxidants (median range: ΣAr-SAs: not calculated due to detection frequency < 30 % (NA)-4.06 ng/g, wet weight (ww); 26DTBP: NA-1.91 ng/g ww), UV absorbents (median range: ΣBZT-UVs: NA-8.71 ng/g ww; ΣUVFs: NA-48.3 ng/g ww) generally showed greater concentrations in the liver of these species. Seabirds accumulated higher levels of these contaminants (median range: ΣBZT-UVs: 3.38-8.71 ng/g ww; ΣUVFs: NA-48.3 ng/g ww; ΣAr-SAs: 0.07-4.06 ng/g ww; 26DTBP: NA-1.14 ng/g ww)) than the other groups (median range: ΣBZT-UVs: NA-1.31 ng/g ww; ΣUVFs: NA-4.22 ng/g ww; ΣAr-SAs: NA; 26DTBP: NA-1.91 ng/g ww), suggesting that seabirds may be useful indicator species for future long-term monitoring. The livers of Arctic char in the Canadian Arctic generally contain lower levels of these contaminants than those of freshwater fish in temperate regions. Spatial variations were found in the liver of black guillemots, Hudson Bay polar bears, and landlocked char for some target contaminants, indicating differences in the levels of these contaminants in their surrounding environment or diet. Consumption of liver tissues from these species may expose humans to varying levels of UV absorbents and industrial antioxidants. This study establishes a baseline for future research of the spatial and temporal trends of these contaminants in Arctic species. It provides the basis for elucidating the fate of these contaminants and assessing their adverse effects at environmental-relevant concentrations in the Arctic. Factors influencing the accumulation patterns of these contaminants in Arctic biota and their potential health risks require further investigation.

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)0.000
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 score0.185
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.226
Teacher spread0.206 · 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

Citations17
Published2024
Admission routes3
Has abstractyes

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