Plastic pollution in the Arctic
Bibliographic record
Abstract
Plastic pollution is now pervasive in the Arctic, even in areas with no apparent human activity, such as the deep seafloor. In this Review, we describe the sources and impacts of Arctic plastic pollution, including plastic debris and microplastics, which have infiltrated terrestrial and aquatic systems, the cryosphere and the atmosphere. Although some pollution is from local sources — fisheries, landfills, wastewater and offshore industrial activity — distant regions are a substantial source, as plastic is carried from lower latitudes to the Arctic by ocean currents, atmospheric transport and rivers. Once in the Arctic, plastic pollution accumulates in certain areas and affects local ecosystems. Population-level information is sparse, but interactions such as entanglements and ingestion of marine debris have been recorded for mammals, seabirds, fish and invertebrates. Early evidence also suggests interactions between climate change and plastic pollution. Even if plastic emissions are halted today, fragmentation of legacy plastic will lead to an increasing microplastic burden in Arctic ecosystems, which are already under pressure from anthropogenic warming. Mitigation is urgently needed at both regional and international levels to decrease plastic production and utilization, achieve circularity and optimize solid waste management and wastewater treatment. Plastic debris and microplastics are ubiquitous in the Arctic. This Review describes the sources, distribution and consequences of this pollution, and calls for immediate action to mitigate further ecosystem impact.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".