Factors Influencing Mercury Exposure in Arctic-Breeding Shorebirds
Bibliographic record
Abstract
Abstract Mercury (Hg) pollution remains a concern to Arctic ecosystems. The objective of this study was to identify factors influencing Hg concentrations in Arctic-breeding shorebirds and highlight regions and species at greatest risk of Hg exposure. We analyzed 2,478 blood and feather samples from 12 shorebird species breeding at nine sites across the North American Arctic during 2012 and 2013. Blood Hg concentrations, which reflect Hg exposure in the local area in individual shorebirds: 1) ranged from 0.01–3.52 µg/g, with an overall mean of 0.30 ± 0.27 µg/g; 2) were influenced by species and study site, but not sampling year, with birds sampled near Utqiaġvik, AK, having the highest concentrations; and 3) were influenced by foraging habitat at some sites. Feather Hg concentrations, which reflected Hg exposure from the wintering grounds, were generally higher than blood, ranging from 0.07–12.14 µg/g in individuals, with a mean of 1.14 ± 1.18 µg/g. Feather Hg concentrations were influenced by species and year. Most Arctic-breeding shorebirds had blood and feather Hg concentrations at levels where no adverse effects of exposure were likely, though some individuals sampled near Utqiaġvik had Hg levels that are certainly of concern. Overall, these data increase our understanding of how Hg is distributed in the various habitats of the Arctic, and what factors predispose Arctic-breeding shorebirds to Hg, and lay the foundation for future monitoring efforts.
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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 | 0.001 |
| 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 | 0.000 |
| 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".