Methylmercury production in a chronically sulfate-impacted sub-boreal wetland
Bibliographic record
Abstract
Increased deposition of atmospheric sulfate exacerbates methylmercury (MeHg) production in freshwater wetlands by stimulating methylating bacteria, but it is unclear how methylation in sub-boreal wetlands is impacted by chronically elevated sulfate inputs, such as through mine discharges. The purpose of our study is to determine how sulfate discharges to wetlands from iron mining activities impact MeHg production. In this study, we compare spatial and temporal patterns in MeHg and associated geochemistry in two wetlands receiving contrasting loads of sulfate. Two orders of magnitude less sulfate in the un-impacted wetland create significant differences in acid-volatile sulfide and porewater sulfide; however, dissolved and solid-phase MeHg concentrations and methylation rate potentials (Kmeth) are statistically similar in both wetlands. Permitted mine pumping events flood the sulfate-impacted wetland with very high sulfate waters during the fall. In contrast to observations in sulfate-limited systems, this large input of sulfate to a chronically sulfate-impacted system led to significantly lower potential relative methylation rates, suggesting a predominance of demethylation processes over methylation processes during the sulfate loading. Overall, short-term measurements of methylation and demethylation potential are unrelated to gross measures of long-term MeHg accumulation, indicating a decoupling of short- and long-term process measurements and an overall disequilibrium in the systems. High sulfide accumulation, above ∼600-800 μg l(-1) sulfide, in the sulfate-impacted system lowers long-term MeHg accumulation, perhaps as a result of less bioavailable Hg-S complexes. Although continued research is required to determine how sulfate-limited freshwater wetlands might respond to new, large inputs of high-sulfate runoff from mining operations, chronically impacted wetlands do not appear to continually accumulate or produce MeHg at rates different from wetlands unimpacted by mining.
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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".