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Record W3118661965 · doi:10.1038/s41467-020-20347-1

Pervasive distribution of polyester fibres in the Arctic Ocean is driven by Atlantic inputs

2021· article· en· W3118661965 on OpenAlexafffund
Peter S. Ross, Stephen Chastain, Ekaterina Vassilenko, Anahita Etemadifar, Sarah Zimmermann, Sarah-Ann Quesnel, Jane Eert, Eric Solomon, Shreyas Patankar, Anna Posacka, Bill Williams

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsPolyesterArcticThe arcticOceanographyDistribution (mathematics)ChemistryGeology

Abstract

fetched live from OpenAlex

Microplastics are increasingly recognized as ubiquitous global contaminants, but questions linger regarding their source, transport and fate. We document the widespread distribution of microplastics in near-surface seawater from 71 stations across the European and North American Arctic - including the North Pole. We also characterize samples to a depth of 1,015 m in the Beaufort Sea. Particle abundance correlated with longitude, with almost three times more particles in the eastern Arctic compared to the west. Polyester comprised 73% of total synthetic fibres, with an east-to-west shift in infra-red signatures pointing to a potential weathering of fibres away from source. Here we suggest that relatively fresh polyester fibres are delivered to the eastern Arctic Ocean, via Atlantic Ocean inputs and/or atmospheric transport from the South. This raises further questions about the global reach of textile fibres in domestic wastewater, with our findings pointing to their widespread distribution in this remote region of the world.

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)0.000
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 score0.137
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations288
Published2021
Admission routes2
Has abstractyes

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