Arsenate decreases production of methylmercury across increasing sulfate concentration amendments in freshwater lake sediments
Bibliographic record
Abstract
Arsenic (As) and sulfate pollution are often found co-occurring as a result of smelting metal ores. Previous studies showed that sulfate reducing microbes (SRMs) can use As(V) as a terminal electron acceptor, while others reported that SRMs are the main mercury (Hg) methylators in freshwater systems. However, we have yet to fully explore how As(V) can affect methylmercury (MeHg) production. In this study, we examined whether additions of As(V) and sulfate in freshwater sediments collected near a major gold mine with a history of S and As emissions affect Hg methylation. First, we show that Hg methylation in lake sediments was primarily limited by carbon substrate availability rather than by that of sulfate as terminal electron acceptors. Then, under conditions where carbon is not limiting, sulfate addition to the system significantly increased Hg methylation rate constants. Finally, we show that MeHg production rates in sediments significantly decreased with increasing As(V) concentrations, regardless of the sulfate concentration amended to sediments. This work underscores the apparent antagonistic effects of As(V) on the one hand, and carbon and sulfate on the other hand on the kinetics of Hg methylation. Arsenic controls on Hg methylation are complex and a combination of direct impact on the methylators' fitness, the formation of As-bearing mineral phases affecting Hg bioavailability, or changes in the microbial community structures over increasing As concentrations should be the focus of additional investigations.
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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".