Diffusion of <scp> H <sub>2</sub> S </scp> from anaerobic thiolated ligand biodegradation rapidly generates bioavailable mercury
Bibliographic record
Abstract
Summary Methylmercury is a potent neurotoxin that biomagnifies through food webs and which production depends on anaerobic microbial uptake of inorganic mercury (Hg) species. One outstanding knowledge gap in understanding Hg methylation is the nature of bioavailable Hg species. It has become increasingly obvious that Hg bioavailability is spatially diverse and temporally dynamic but current models are mostly built on single thiolated ligand systems, omitting ligand exchanges and interactions, or the inclusion of dissolved gaseous phases. In this study, we used a whole‐cell anaerobic biosensor to determine the role of a mixture of thiolated ligands on Hg bioavailability. Serendipitously, we discovered how the diffusion of trace amounts of exogenous biogenic H 2 S, originating from anaerobic microbial ligand degradation, can alter Hg speciation – away from H 2 S production site – to form bioavailable species. Regardless of its origins, H 2 S stands as a mobile mediator of microbial Hg metabolism, connecting spatially separated microbial communities. At a larger scale, global planetary changes are expected to accelerate the production and mobilization of H 2 S and Hg, possibly leading to increased production of the potent neurotoxin; this work provides mechanistic insights into the importance of co‐managing biogeochemical cycle disruptions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".