Biogeochemical factors influencing net mercury methylation in freshwater systems
Bibliographic record
Abstract
Mercury methylation in aquatic systems has been linked to the activity of various anaerobic microbes, including sulfate-reducers (SRB), iron-reducers (FeRP) and methanogens (MPA). This study focuses on the biogeochemical factors, i.e., the relative importance of the diverse groups of anaerobic microbes, that affect net methyl mercury formation in freshwater. Methylation and demethylation were measured separately using enriched stable isotopes of mercury in microcosms treated with specific microbial inhibitors and abiotic control systems. Non-contaminated sediments from the Mer Blue wetland in Ottawa, Ontario, were used to test the proper set up and methods to be used for future experiments. Mercury-contaminated sediments of the St. Lawrence River (SLR) in Cornwall (Zone 1), Ontario, were investigated because they have been found to be a potential source of MeHg in the food web and the river system. In the Zone 1 SLR sediments, strong positive correlations were observed between methylation rate constants and sulfate reducing rates, as well as demethylation rates constants and methane production rates, indicating that SRB are primary methylators and MPA have the leading role in methylmercury demethylation. The inhibition of both SRB and MPA enhanced iron-reduction while MeHg production was not completely stopped, indicating that iron-reduction might however be another important process in MeHg production in the Zone 1 SLR sediments, probably by decreasing demethylation rather than favouring methylation, as shown by the strong negative correlation between Kd and iron-reduction rates. Similar findings were obtained for the Mer Bleue sediments, with the exception that SRB were involved in both methylation and demethylation. A new modified procedure for measuring mercury isotopes in sediment samples was also proposed. The procedure uses acid leaching-ion exchange-thiosulfate extraction (TSE) to isolate and purify the methylated mercury from the matrix. Major advantages of the TSE procedure include the extraction and analysis of a large number of samples in a short time, excellent analyte recoveries, and the lack of artefact formation. Recoveries between 94 and 106% were obtained for the standards CRMs, BCR 580 and IAEA 405. Comparisons between TSE and other procedures (distillation, acid-leaching) have shown good agreement of methylmercury values.
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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 | 0.001 | 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".