Microplastics around an Arctic seabird colony: Particle community composition varies across environmental matrices
Bibliographic record
Abstract
Plastic pollution is a contaminant of global concern, as it is present even in remote ecosystems - like the Arctic. Arctic seabirds are vulnerable to ingesting plastic pollution, and these ingested particles are shed in the form of microplastics via guano. This suggests that Arctic seabird guano may act as a vector for the movement of microplastics into and around northern ecosystems. While contaminant-laden guano deposition patterns create a clear concentration gradient of chemicals around seabird colonies, this has not yet been investigated with plastic pollution. Here we tested whether a contaminant gradient of plastic pollution exists around a seabird colony that is primarily comprised of northern fulmars (Fulmarus glacialis) in the Canadian Arctic. Atmospheric deposition, surface water, and surface sediment samples were collected below the cliff-side of the colony and at increasing intervals of 1 km from the colony. Fulmars were also collected when foraging away from their colony. Microplastics and other anthropogenic microparticles were identified in all three environmental matrices as well as fulmar guano. Fibers were the most common shape in fulmar guano, atmospheric deposition and surface sediment, and fragments were the most common shape in surface water. We did not find a gradient of microplastic concentrations in environmental matrices related to distance from the colony. Combined, these results suggest that fulmars are not the primary source of microplastic around this colony. Further research is warranted to understand sources of microplastics to the areas around the colonies, including to what extent seabirds transport and concentrate microplastics in Arctic ecosystems, and whether concentrations proximate to large colonies may be species dependent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".