Hg(II) Adsorption by Bacteria: A Surface Complexation Model and Its Application to Shallow Acidic Lakes and Wetlands in Kejimkujik National Park, Nova Scotia, Canada
Bibliographic record
Abstract
The fate and environmental threat posed by mercury in aquatic systems is controlled, in part, bythe transport of Hg(II) from oxic to anoxic zones in lakes and its subsequent transformation to organic mercury. The transport of Hg(II) in aquatic systems can be affected by its partitioning between the dissolved and particulate phases. In this study, batch experiments were performed to quantify Hg(II) adsorption to Bacillus subtilis as bacteria-to-metal ratio, pH, chloride concentration, growth phase, and reaction time were independently varied. The laboratory data were well described by a surface complexation model (SCM) considering the adsorption of neutral Hg(II) hydroxide and chloride complexes by specific functional groups on the bacterial surface. To evaluate its applicability to complex aquatic systems, the SCM was used to predict the distributions of Hg(II) in 36 shallow acidic lakes and wetlands in Kejimkujik National Park, Nova Scotia, Canada. The lab-derived SCM provided a statistically accurate (r2 = 0.615, P < 0.01) fit to the field data when it was expanded to consider Hg(II) complexation by dissolved organic matter. Inclusion of Hg(II)-mineral adsorption reactions did not improve the fit of the model. The quality of fit provided by the expanded SCM suggested that the major assumptions implicit in applying a lab-derived model to the field were justifiable. Our study has demonstrated that SCMs are powerful tools for dynamic prediction of the sorption of environmental contaminants to biocolloids at the regional scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".