Organic Material: The Primary Control on Mercury Methylation and Ambient Methyl Mercury Concentrations in Estuarine Sediments
Bibliographic record
Abstract
Estuarine environments that have no direct sources of mercury (Hg) pollution may have sediment concentrations of methylmercury (MeHg) as high as those of polluted marine environments. In this study we examined the biogeochemical factors affecting net methylation and sediment MeHg concentrations in an unpolluted estuarine environment, the Ore River estuary, which discharges into the Bothnian Bay (20-120 ng total Hg g(-1) dry sediment, salinity 3-5% per hundred). We analyzed the spatial and temporal differences in surface sediment profiles of MeHg concentration, Hg methylation, MeHg demethylation, and concentrations of sulfide and oxygen between accumulation and erosion type bottoms. The main difference between the bottoms studied was in the proportion of organic material (OM) in the sediment, ranging between 0.8% and 10.8%. The pore water sulfide concentration profiles also differed considerably between sites and seasons, from 0 to 20 microM, with 100 microM as the extreme maximum. The sediment MeHg concentration profiles (0-10 cm) mostly varied between 0.1 and 7 ng g(-1) dry weight (dw, as Hg). The MeHg demethylation rates were relatively low and the depth profiles of the rates were relatively constant over season, site, and depth. In contrast, both rates and depths of maximum Hg methylation differed between the bottoms. The results indicate that the amount of OM accumulated at the bottoms was the main factor affecting net MeHg production, while the total amount of Hg had little or no influence on the amount of MeHg in the sediment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".