Divalent Base Cations Hamper Hg<sup>II</sup> Uptake
Bibliographic record
Abstract
Despite the alarming trends of declining base cation concentrations in boreal lakes, no studies have attempted to predict the consequences of this decline on the geochemical cycle of mercury, a top priority contaminant worldwide. In this study, we used a whole-cell gram-negative bioreporter to evaluate the direction and magnitude of changes in net accumulation of Hg(II) by bacteria in response to changing base cation concentrations. We show that regardless of the speciation of Hg(II) in solution, increasing divalent base cation concentrations decrease net Hg(II) accumulation by the bioreporter, suggesting a protective effect of these cations. Our work suggests that the complexity of the cell wall of gram-negative bacteria must be considered when modeling Hg uptake pathways; we propose that base divalent cations contribute to hamper net Hg(II) accumulation by decreasing outer membrane permeability and, therefore, the passive diffusion of Hg(II) species to the periplasmic space. This work points to an unsuspected and likely harmful consequence of a delay in recovering from acidification in boreal lakes, in that uptake of Hg(II) by bacteria is not only enhanced by the reduced pH but can also be enhanced by a decline in base cation levels.
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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 | 0.001 | 0.001 |
| 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".