Plastic ingestion by seabirds in the circumpolar Arctic: a review
Bibliographic record
Abstract
Plastic pollution is a ubiquitous global environmental problem. Plastic ingestion by seabirds is an increasing issue even in remote areas, such as the Arctic, yet research and monitoring of plastic ingestion in Arctic seabird populations is limited, and there are large knowledge gaps for many geographic regions. There is currently no standard technique for monitoring plastic debris in the Arctic, making it difficult to compare studies and monitor global trends. Here, we review the current state of knowledge of plastic ingestion by seabirds in the Arctic. We analyzed 38 published records that report plastic ingestion by seabirds in the Arctic region. Of the 51 seabird species examined for plastic ingestion in the Arctic, over half have ingested plastic; however, the majority have a limited number of studies, small sample sizes, and (or) data are more than 15 years old. Additionally, the spatial distribution of plastic ingestion reports in the Arctic varies widely, with large knowledge gaps in the northernmost areas of most countries. This indicates that we lack recent information on plastic ingestion for most of the seabird species in the Arctic. Further, less than one-third of studies reference standardized methods from other regions, making it difficult to assess spatial and temporal trends. Long-term monitoring programs should be established in the Arctic to obtain an accurate assessment of plastic ingestion by seabirds in this region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".