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Record W3214956443

First record of synthetic micro-fibre ingestion by Mute Swans Cygnus olor and Whooper Swans C. cygnus

2021· article· en· W3214956443 on OpenAlexfundno aff
Neil E. Coughlan, Connie Baker‐Arney, Kane Brides, Jaimie T. A. Dick, Rose M. Griffith, Craig Holmes, Ryan Johnston, Thomas C. Kelly, Linda Lyne, Eoghan M. Cunningham

Bibliographic record

VenueWildfowl (Wildfowl & Wetlands Trust) · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsIngestionMicroplasticsFisheryBiologyEcologyZoology
DOInot available

Abstract

fetched live from OpenAlex

Despite an acute focus on the ingestion of large and small synthetic debris by seabirds, scant consideration has been given to their occurrence in other avian species inhabiting coastal and inland wetland areas. Here, we assess ingestion of synthetic micro-fibres (i.e. microplastics and other non-natural fibres, 0.5-5 mm in size) by Mute Swans Cygnus olor inhabiting a large freshwater reservoir (n = 12 faecal samples), and from Whooper Swans C. cygnus wintering on a remote offshore Atlantic island (n = 11 faecal samples). Samples were chemically digested to eliminate labile organic matter including natural fibres. In total, 79 synthetic micro-fibres were recovered at frequencies of 4.2 ± 0.8 and 2.6 ± 0.7 (mean ± s.e.) per sample, ranging from 0-10 and 0-7 micro-fibres per sample, for Mute Swan and Whooper Swan faecal samples, respectively. The number of synthetic micro-fibres recovered did not differ significantly between species or sites. Similarly, there was no difference in the number of synthetic micro-fibres detected per gram of faecal sample. Overall, our preliminary data further bolster emerging records for the ingestion of synthetic debris by non-marine waterbirds inhabiting freshwater and coastal areas.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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