Are Methylmercury Concentrations in the Wetlands of Kejimkujik National Park, Nova Scotia, Canada, Dependent on Geology?
Bibliographic record
Abstract
In the relatively pristine ecosystem in Kejimkujik Park, Nova Scotia, methylmercury (MeHg) concentrations in loons, Gavia immer, are among the highest recorded anywhere in the world. This study investigated the influence of bedrock lithology on MeHg concentrations in wetlands. Twenty-five different wetland field sites were sampled over four different bedrock lithologies; Kejimkujik monzogranite, black sulfidic slate, gray slate, and greywacke. Soil samples were analyzed for ethylmercury (EtHg), MeHg, total Hg, acid-volatile sulfides (AVS), organic matter, and water content as well as the biological parameters, mercury methyltransferase (HgMT) activity, sulfate reduction rates, fatty acid methyl ester (FAME) composition, and acidity. Methylmercury concentrations in the wetlands were highly dependent (P < 0.08) on lithology with no significant difference between bogs, fens, and swamps. Methylmercury concentrations in wetland soils developed on Kejimkujik monzogranite averaged 900 ng kg(-1) compared with only 300 ng kg(-1) in wetland soils developed on black sulfidic slate. Fatty acid methyl ester composition was also lithologically dependent (P < 0.001) with biomarkers for Desulfobulbus spp. discriminating between sites containing high and low MeHg concentrations. Levels of MeHg in wetlands were predicted mainly (41% of the sum of squares) by HgMT activity that differed (P < 0.009) between wetlands, with activity in bogs almost three times that present in swamps. Wetland MeHg concentrations are highly dependent on the lithology on which they have developed for largely biological reasons.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".