Relationship between Extracellular Low-Molecular-Weight Thiols and Mercury Species in Natural Lake Periphytic Biofilms
Bibliographic record
Abstract
The uptake of mercury by microorganisms is a key step in the production of methylmercury, a biomagnifiable toxin. Mercury complexation by low-molecular-weight (LMW) thiols can affect its bioavailability and thus the production of methylmercury. Freshwater biofilms were sampled in the summer using artificial Teflon substrates submerged for over a year to allow natural community colonization in the littoral zone of a Boreal Shield lake. Inside biofilms, concentrations of different extracellular thiol species (thioglycolic acid, l-cysteine-l-glycine, cysteine, and glutathione) were up to 3 orders of magnitude greater than in the surrounding water column, potentially more readily controlling mercury speciation than in the water column. All biofilm thiols except thioglycolic acid were highly correlated to chlorophyll a, likely indicating an algal origin. Extracellular total mercury represented 3 ± 1% of all biofilm mercury and was preferentially found in the capsular fraction. Levels of LMW thiols of presumed algal origins were highly correlated with total mercury in the mobile colloidal fraction of biofilms. We propose that periphytic phototrophic microorganisms such as algae likely affect the bioavailability of mercury through the exudation of LMW thiols, and thus they may play a key role in the production of methylmercury in biofilms.
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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) | 0.000 | 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".