Review of plastic pollution policies of Arctic countries in relation to seabirds
Bibliographic record
Abstract
Marine plastic is a ubiquitous environmental problem that can have an impact on a variety of marine biota, such as seabirds, making it an important concern for scientists and policy makers. Although research on plastic ingestion by seabirds is increasing, few studies have examined policies and long-term monitoring programs to reduce marine plastic in the Arctic. This paper provides a review of international, national, and regional policies and long-term monitoring programs that address marine plastic in relation to seabirds in the Arctic countries: Canada, the Kingdom of Denmark (Greenland and the Faroe Islands), Finland, Iceland, Norway, the Russian Federation, Sweden, and the United States of America. Results show that a broad range of international, national, regional and local policies address marine debris, specifically through waste management and the prevention of pollution from ships. However, few policies directly address seabirds and other marine biota. Further, policies are implemented inconsistently across regions, making it difficult to enforce and monitor the efficacy of these policies given the long-range transport of plastic pollution globally. To reduce marine plastic pollution in the Arctic environment, pan-Arctic and international collaboration is needed to implement standardized policies and long-term monitoring programs for marine plastic in the Arctic and worldwide.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".