Microbial Reduction and Oxidation of Mercury in Freshwater Lakes
Bibliographic record
Abstract
The evasion of elemental mercury represents a significant pathway for reducing the level of this potentially toxic material in aquatic ecosystems. The evasion rate is controlled by the concentration of dissolved gaseous mercury (DGM) across the air-water interface, water, and air temperature as well as wind speed. Here we investigate the role of microbial mercury oxidation and reduction in regulating DGM diel patterns in two freshwater lakes, Jack's Lake and Lake Ontario. Three replicate diurnal cycles of DGM in Brookes Bay, Jack's Lake peaked at 313 fM between 9:00 to 10:30 and decreased to 79.6 fM by 16:00. Microbial mercury reductase activity (converts Hg2+ to Hg0) increased with DGM concentrations and mercury oxidase activity (converts Hg0 to Hg2+) increased as DGM concentrations decreased in the mid-afternoon. This illustrates that mercury oxidase activity was linked to hydrogen peroxide (H2O2) diurnal patterns. Thirty minutes after spiking Lake Ontario water with H2O2, mercury oxidase activity increased by 250% and by 60 min, DGM decreased to 28% of its initial value. Two hours after the H2O2 spike, mercury oxidase activity had declined, but mercury reductase activity and DGM both increased. Four hours after the spike, mercury reductase and DGM levels had returned to original levels. Our results are consistent with the following sequence of events. In the morning, microbial activity produces DGM (in addition to any DGM formed through photoreduction of Hg2+). As photochemically produced H2O2 increases in concentration it induces the biologically mediated decrease in DGM concentrations throughout the afternoon. To predict concentration of DGM in surface waters and flux rates to the atmosphere, the contribution of photoreduction and photooxidation must be placed in context with reduction and oxidation rates due to microbial activity.
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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 | 0.001 | 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".