Elevated organic UV absorbents in herring gull eggs near urban areas: Spatial trends across the St. Lawrence system
Bibliographic record
Abstract
Ultraviolet (UV) absorbents, such as benzotriazole UV stabilizers (BZT-UVs) and UV filters (UVFs), are mass-produced additives and contaminants of emerging concern. Monitoring seabird eggs provides valuable insights into geographic patterns of contaminant exposure across ecosystems. 8 BZT-UVs and 5 UVFs in herring gull (Larus argentatus) eggs were collected in 2022-2023 from 9 colonies across three sections (fluvial, estuary, gulf) of the St. Lawrence, one of North America's most industrialized regions. Two BZT-UVs (UV-328 and UV-329) and two UVFs (EHS and HMS) were the predominant contaminants across the system. Concentrations of UV-328 (median: 2.6 ng/g wet weight (ww)), UV-329 (median: 2.2 ng/g (ww)), and EHS (median: 5.6 ng/g (ww)) were the highest known to be reported to date in seabird eggs. Total BZT-UVs, UV-328, and UV-329 were highest near Montreal, with concentrations decreasing downstream in the estuary and gulf, correlating positively with human population density and wastewater treatment plant effluent volume. These findings suggest that anthropogenic activities across the St. Lawrence influence BZT-UV exposure in herring gulls. UVF concentrations were similar across the studied range, suggesting that factors other than the local landscape influence contamination patterns. This study provides the first overview of spatial trends in UV absorbent contamination in seabird eggs, underscoring their value as sentinels of emerging contaminants. Further investigations are required to understand the potential risks these contaminants pose to biota.
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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 | 0.000 | 0.000 |
| 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".