Dissolved Organic Matter Kinetically Controls Mercury Bioavailability to Bacteria in Lake Water from the Canadian Arctic
Bibliographic record
Abstract
The repercussions of rapid climate-change are felt worldwide, but particularly in Arctic and Subarctic regions. Evidence of recent changes in water chemistry is being recorded in Arctic aquatic ecosystems, bringing further attention to contaminant dynamics in these environments. I assessed the role of dissolved organic matter (DOM) in controlling the bioavailability of mercury (Hg), a top priority among Arctic contaminants, to aquatic food webs using a bacterial bioreporter under oxic conditions. Experiments were performed under pseudo- and non-equilibrium conditions, in both defined media and water samples from tundra lakes with a large gradient in DOM. Inorganic HgII was considerably more bioavailable under non-equilibrium conditions than when DOM was absent or when HgII and DOM had reached pseudoequilibrium (24h). Under these enhanced uptake conditions, HgII bioavailability followed a bell shaped curve as DOM concentrations increased, both for defined media and field samples, suggesting that complexation kinetics and binding thresholds on DOM determine HgII bioavailability to methylating bacteria, and likely MeHg concentrations, the bioaccumulative neurotoxic form of Hg. Experiments also suggest that DOM may alter cell wall properties to facilitate the first steps toward HgII internalization via facilitated or active transport, and yet without altering overall cell wall permeability. While further research on ternary (HgII-cell-DOM) interaction is warranted, I propose a molecular shuttle model for DOM in facilitating bacterial HgII uptake, and the existence of a short-lived yet critical time window (<24h) during which DOM facilitates the entry of newly deposited HgII from the atmosphere into aquatic food webs.
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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 | 0.001 | 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) | 0.001 | 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".