Mercury in soil, sediment, and lake water in upland headwater catchments in Ireland
Bibliographic record
Abstract
Abstract Mercury concentrations in the atmosphere, surface waters, and soils have increased beyond natural levels as a result of human activities, which poses a risk to human and environmental health. Ireland is situated on the western periphery of Europe, and it is generally assumed that environmental mercury is low but there is limited information on mercury within natural environments. In this study the interlinkages in the concentration of mercury in soils, lake sediment, and lake water were investigated in remote upland acid-sensitive catchments influenced by low deposition of atmospheric mercury. Thirty-one upland lake catchments were sampled for topsoil, lake sediment, and lake water during 2017–2018. Total mercury (THg) concentrations in water (median 5.4 ng/L) and sediment (median 30 ng/g) were low, owing to the remote location of the lakes. In contrast, THg in catchment soil was relatively high for a background region (median: 240 ng/g), which was attributed to the high organic matter content of the soil (median 87%). The results suggest catchment soils are the dominant driver of variation in THg in lake water and sediment in upland regions in Ireland. Further, given that highly organic soils can obscure interlinkages within catchments, this study demonstrated the value of exploring mercury independent of the influence of organic matter, i.e., THg normalized by organic carbon. As global action (such as the Minamata Convention) decreases mercury emissions, legacy soil mercury may become a more important input to aquatic systems than atmospheric deposition, which makes the relationships between terrestrial catchments and their water bodies vital for our understanding of mercury cycling.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".