Plastic ingestion in thick-billed murres (Uria lomvia) from the Canadian high Arctic
Bibliographic record
Abstract
Plastic pollution continues to prevail across Arctic marine environments, readily available for marine organisms to ingest, especially seabirds. Seabird plastic ingestion datasets often lack standardized time series that allow for trend analysis. We examine the recent status of plastic ingestion (≥ 1 mm) in thick-billed murres ( Uria lomvia ) using standardized methods to assess spatial differences and time trends over two decades (2007–2021). In 2021, 7 % of the 43 thick-billed murres we examined from this region ingested plastic; 1 piece of plastic in three separate individuals. No significant temporal or spatial differences in murre plastic ingestion were observed. Therefore, plastic pollution (≥ 1 mm) currently poses a low risk to Arctic breeding murres. The long-term monitoring of plastic ingestion in this species should continue as murres have economic and cultural importance to communities and provide insight to marine plastic pollution trends. • We examined time trends of plastic ingestion in thick-billed murres ( Uria lomvia ). • Murres indicate potential plastic pollution below the surface of the water. • Ingestion occurrence was 7 % in murres from the Canadian high Arctic in 2021. • Plastic pollution poses a low risk for Arctic breeding murres. • Standardized methods allow for future comparisons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".