Mobility and transport of mercury and methylmercury in peat as a function of changes in water table regime and plant functional groups
Bibliographic record
Abstract
Abstract Climate change is likely to significantly affect the hydrology, ecology, and ecosystem function of peatlands, with potentially important but unclear impacts on mercury mobility within and transport from peatlands. Using a full‐factorial mesocosm approach, we investigated the potential impacts on mercury mobility of water table regime changes (high and low) and vegetation community shifts (sedge‐dominated, Ericaceae‐dominated, or unmanipulated control) in peat monoliths at the PEATcosm mesocosm facility in Houghton, Michigan. Lower and more variable water table regimes and the loss of Ericaceae shrubs act significantly and independently to increase both total Hg and methylmercury concentrations in peat pore water and in spring snowmelt runoff. These differences are related to enhanced peat decomposition and internal regeneration of electron acceptors which are more strongly related to water table regime than to plant community changes. Loss of Ericaceae shrubs and an increase in sedge cover may also affect Hg concentrations and mobility via oxygen shuttling and/or the provision of labile root exudates. Altered hydrological regimes and shifting vegetation communities, as a result of global climate change, are likely to enhance Hg transport from peatlands to downstream 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) | 0.001 | 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".