Moisture contents regulate peat water-leachable concentrations of methylmercury, inorganic mercury, and dissolved organic matter from boreal peat soils
Bibliographic record
Abstract
Boreal peatlands are “hotspots” of net methylmercury (MeHg) production and may become drier in the future due to climate change. This study investigates a critical gap by analyzing the nuanced relationship between soil moisture content and the release of MeHg, inorganic mercury (IHg), sulfate (SO42-), and dissolved organic matter (DOM) in a laboratory incubation of boreal peat soils. Dried peat soils exhibited heightened releases of IHg, MeHg, and SO42- during re-wetting events. Both dried and saturated peat soils released more DOM than moist peat soils during re-wetting events, and DOM released from dried soils had higher bioaccessibility than that from the saturated soils (p<0.05). There was an equilibrium of IHg concentrations between peat soils and pore waters, but long-term severe drought may disrupt this equilibrium and then release more IHg to pore waters during re-wetting events. Contrary to expectations, positive relationships between IHg concentrations and SUVA254 did not exist in all treatments. MeHg and SO42- were depleted quickly because there was no external input of Hg and SO42- to this static system. More bioaccessible DOM than aromatic DOM was released from peat soils with different soil moisture contents after 32 weeks during the re-wetting event (p<0.05). These results imply that re-wetting of peat soils after droughts can increase the release of MeHg from peat soils and may also increase net MeHg production due to the release of SO42- and bioaccessible DOM from peat soils, reshaping our understanding of soil moisture's role in mercury dynamics. This novel insight into soil moisture and MeHg dynamics carries significant implications for mitigating mercury contamination in aquatic ecosystems.
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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".