Co-contaminants of microplastics in two seabird species from the Canadian Arctic
Bibliographic record
Abstract
Through ingestion and subsequent egestion, Arctic seabirds can bioaccumulate microplastics at and around their colony breeding sites. While microplastics in Arctic seabirds have been well documented, it is not yet understood to what extent these particles can act as transport vehicles for plastic-associated contaminants, including legacy persistent organic pollutants (POPs), trace metals, and organic additives. We investigated the occurrence and pattern of organic and inorganic co-contaminants of microplastics in two seabird species from the Canadian Arctic — northern fulmar (Fulmarus glacialis) and black-legged kittiwake (Rissa tridactyla). We found that fulmars had higher levels of plastic contamination and emerging organic compounds (known to be plastic additives) than kittiwakes, whereas higher concentrations of legacy POPs were found in kittiwakes than the fulmars. Furthermore, fulmars, the species with the much larger foraging range (∼200 km), had higher plastic pollution and overall contaminant burdens, indicating that birds may be acting as long-range transport vectors for plastic-associated pollution. Our results suggest a potential connection between plastic additive contamination and plastic pollution burdens in the bird stomachs, highlighting the importance of treating plastic particles and plastic-associated organic additives as co-contaminants rather than separate pollution issues.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 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".