Plant mercury accumulation and litter input to a Northern Sedge-dominated Peatland
Bibliographic record
Abstract
Plant foliage plays an essential role in accumulating mercury (Hg) from the atmosphere and transferring it to soils in terrestrial ecosystems, and many studies have focused on forested ecosystems. Hg input from plants to northern peatland peat soils has not been nearly as well studied and is likely equally important from a mass balance perspective. In this study, we investigated the accumulation of atmospheric Hg by the dominant plant species, few-seeded sedge ( Carex oligosperma Michx.), wire sedge ( Carex lasiocarpa Ehrh), tussock sedge ( Carex stricta Lamb.), and sweet gale ( Myrica gale L.), in a boreal sedge-dominated peatland. Foliar Hg concentrations decreased early in the growing season due to growth dilution, and after that they were subsequently positively correlated with leaf age (time). Hg concentrations were 1.4–1.7 times higher in sweet gale than in sedges. A leaching experiment showed that sweet gale leached less Hg but more bioaccessible dissolved organic matter (DOM) by mass than sedges. Leaching of Hg was positively related to the aromaticity of DOM in leachate, suggesting the importance of DOM with higher aromaticity in controlling Hg mobility. Annual inputs of Hg through senesced leaf material to peat soils were 9.88, 1.62, and 8.29 mg ha −1 yr −1 for sweet gale, tussock sedge, and few-seeded sedge and wire sedge, respectively. Future investigations into foliar Hg accumulation and input from other plant species to the sedge-dominated peatland are needed to estimate the annual Hg inputs precisely.
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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".