Mercury and Sulfur Redox Cycling Affect Methylmercury Levels in Rice Paddy Soils across a Contamination Gradient
Bibliographic record
Abstract
Methylmercury (MeHg) contamination in rice via paddy soils is an emerging global environmental issue. An understanding of mercury (Hg) transformation processes in paddy soils is urgently needed in order to control Hg contamination of human food and related health impacts. Sulfur (S)-regulated Hg transformation is one important process that controls Hg cycling in agricultural fields. In this study, Hg transformation processes, such as methylation, demethylation, oxidation, and reduction, and their responses to S input (sulfate and thiosulfate) in paddy soils with a Hg contamination gradient were elucidated simultaneously using a multi-compound-specific isotope labeling technique ( 200 Hg II, Me 198 Hg, and 202 Hg 0 ). In addition to Hg II methylation and MeHg demethylation, this study revealed that microbially mediated reduction of Hg II, methylation of Hg 0, and oxidative demethylation–reduction of MeHg occurred under dark conditions; these processes served to transform Hg between different species (Hg 0, Hg II, and MeHg) in flooded paddy soils. Rapid redox recycling of Hg species contributed to Hg speciation resetting, which promoted the transformation between Hg 0 and MeHg by generating bioavailable Hg II for fuel methylation. Sulfur input also likely affected the microbial community structure and functional profile of Hg II methylators and, therefore, influenced Hg II methylation. The findings of this study contribute to our understanding of Hg transformation processes in paddy soils and provide much-needed knowledge for assessing Hg risks in hydrological fluctuation-regulated 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".