Plastic ingestion, accumulated heavy metals, and health metrics of four Larus gull species feeding at a coastal landfill in eastern Canada
Bibliographic record
Abstract
The objectives of this research were to assess ingested plastics and accumulated heavy metals in four urban gull species. Additionally, the relationships between ingested plastics and selected demographic and health metrics were assessed. Between 2020-2021 during the non-breeding seasons, 105 gulls (46 American herring gulls (HERG, Larus argentatus smithsonianus), 39 great black-backed gulls (GBBG, Larus marinus), 16 Iceland gulls (Larus glaucoides), 4 glaucous gulls (Larus hyperboreus)) were killed at a landfill in coastal Newfoundland and Labrador, Canada, as part of separate, permitted kill-to-scare operations related to aircraft safety. Birds were necropsied, the upper gastrointestinal tract contents were processed using standard techniques, and livers were analyzed for accumulated As, Cd, Hg, and Pb. The relationships between ingested plastics, demographics, and health metrics were assessed in HERG and GBBG. Across all four species, 85 % of birds had ingested at least one piece of anthropogenic debris, with 79 % ingesting at least one piece of plastic. We detected interspecific differences in plastic ingestion and hepatic trace metals, with increased ingested plastics detected in GBBG compared with HERG. For GBBG, levels of ingested plastic were relatively greater for birds with higher scaled mass index, while HERG with more ingested plastic had higher liver lead concentrations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".