Determining feather methylmercury levels in six species of Arctic marine birds
Bibliographic record
Abstract
Although the Canadian Arctic appears to be a region removed from the harmful effects of pollution, environmental contaminants occur in significant concentrations, particularly in marine ecosystems. Mercury (Hg) levels especially have been of increasing concern in the Canadian Arctic. Current attempts to quantify bio-accumulated mercury often involve invasive sampling methods. The objectives of this study were to determine: (a) whether Hg was higher in feathers from top avian predators and scavengers than species feeding lower in marine food chains, similar to patterns found in their eggs; and (b) whether the non-invasive method of sampling feathers is an appropriate alternative to invasive methods. Methyl Hg and total Hg levels were determined from feather samples of six species of Arctic marine birds: Thick-Billed Murre (Uria lomvia), Northern Fulmar (Fulmarus glacialis), Black-Legged Kittiwake (Rissa tridactyla), Ivory Gull (Pagophila eburnea), Glaucous Gull (Larus hyperboreus), and Common Eider (Somateria mollissima borealis). Consistent with my hypothesis, Hg levels were markedly greater in feathers for species that fed higher in the food web in Arctic marine environments. The ratio of methylmercury to total mercury concentrations was surprisingly low in Ivory Gull samples, a result possibly related to photodemethylation in semi-opaque tissues. Consequently, sampling feathers for mercury concentrations appears to be a suitable alternative to employing invasive methods to assess and monitor mercury 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 | 0.000 |
| 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) | 0.000 | 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".