Inter-annual variation of mercury in aquatic bird eggs and fish from a large subarctic lake under a warming climate
Bibliographic record
Abstract
Understanding changes in environmental mercury concentrations is important for assessing the risk to human and wildlife populations from this potent toxicant. Here, we use herring gull (Larus argentatus) eggs to evaluate temporal changes in total mercury (THg) availability from two locations on Great Slave Lake (GSL), Northwest Territories, Canada. Egg THg concentrations increased through time, but this change was due to shifts in gull diets. Stable nitrogen isotopes allowed adjustment of egg THg concentrations for dietary changes. Diet-adjusted egg THg concentrations showed no long-term trend. Consistent with that result, new statistical analysis of THg concentrations in three species of GSL fish showed minor or no temporal changes. Although a long-term trend was absent, inter-year differences in adjusted egg THg concentrations persisted. Contributions of environmental variables (i.e., river flow, lake level, air temperature, precipitation, and wildfire) to these differences were investigated. Egg THg concentrations were greater following years of lower lake levels and greater wildfire extent. Lake level could have affected mercury methylation. Increased wildfire could have enhanced terrestrial Hg releases to the atmosphere where it was transported long distances to GSL. Climate change may increase wildfire extent with impacts on Hg bioaccumulation in northern ecosystems. Egg Hg levels reported here are unlikely to pose health risks to gulls, but in light of ongoing environmental change, monitoring should continue. Our study emphasizes the importance of ancillary datasets in elucidating Hg trends; such information will be critical for evaluating the effectiveness of Hg mitigation strategies implemented as part of the Minamata Convention.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".