Dissolved Organic Matter Kinetically Controls Mercury Bioavailability to Bacteria
Bibliographic record
Abstract
Predicting the bioavailability of inorganic mercury (Hg) to bacteria that produce the potent bioaccumulative neurotoxin monomethylmercury remains one of the greatest challenges in predicting the environmental fate and transport of Hg. Dissolved organic matter (DOM) affects mercury methylation due to its influence on cell physiology (as a potential nutrient) and its influence on Hg(II) speciation in solution (as a complexing agent), therefore controlling Hg bioavailability. We assessed the role of DOM on Hg(II) bioavailability to a gram-negative bacterium bioreporter under oxic pseudo- and nonequilibrium conditions, using defined media and field samples spanning a wide range of DOM levels. Our results showed that Hg(II) was considerably more bioavailable under nonequilibrium conditions than when DOM was absent or when Hg(II) and DOM had reached pseudoequilibrium (24 h) prior to cell exposure. Under these enhanced uptake conditions, Hg(II) bioavailability followed a bell shaped curve as DOM concentrations increased, both for defined media and natural water samples, consistent with bioaccumulation results in a companion paper (this issue) observed for amphipods. Experiments also suggest that DOM may not only provide shuttle molecules facilitating Hg uptake, but also alter cell wall properties to facilitate the first steps toward Hg(II) internalization. We propose the existence of a short-lived yet critical time window (<24 h) during which DOM facilitates the entry of newly deposited Hg(II) into aquatic food webs, suggesting that the bulk of mercury incorporation in aquatic food webs would occur within hours following its deposition from the atmosphere.
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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) | 0.000 | 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 | 0.000 |
| 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".