Methanogens: Principal Methylators of Mercury in Lake Periphyton
Why this work is in the frame
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Bibliographic record
Abstract
Mercury methylation and demethylation rates were measured in periphyton biofilms growing on submerged plants from a shallow fluvial lake located along the St. Lawrence River (Quebec, Canada). Incubations were performed in situ within macrophytes beds using low-level spikes of (199)HgO and Me(200)Hg stable isotopes as tracers. To determine which microbial guilds are playing a role in these processes, methylation/demethylation experiments were performed in the absence and presence of different metabolic inhibitors: chloramphenicol (general bacteriostatic inhibitor), molybdate (sodium molybdate, a sulfate reduction inhibitor), BESA (2-bromoethane sulfonic acid, a methanogenesis inhibitor), and DCMU (3-(3,4-dichlorophenyl)-1,1 dimethyl urea, a photosynthesis inhibitor). Active microbes of the periphytic consortium were also characterized using 16S rRNA gene sequencing. Methylation rates in the absence of inhibitors varied from 0.0015 to 0.0180 d(-1) while demethylation rates ranged from 0.083 to 0.217 d(-1), which corresponds to a net methylmercury balance of -0.51 to 1.28 ng gDW periphyton(-1) d(-1). Methylation rates were significantly decreased by half by DCMU and chloramphenicol, totally inhibited by BESA, and were highly stimulated by molybdate. This suggests that methanogens rather than sulfate reducing bacteria were likely the primary methylators in the periphyton of a temperate fluvial lake, a conclusion supported by the detection of 16S rRNA gene sequences that were closely related to those of methanogens. This first clear demonstration of methanogens' role in mercury methylation in environmental periphyton samples expands the known diversity of microbial guilds that contribute to the formation of the neurotoxic substance methylmercury.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it