Total mercury in the water and sediments of St. Lawrence River wetlands compared with inland wetlands of Temagami - North Bay and Muskoka-Haliburton
Bibliographic record
Abstract
The concentration of total Hg was compared among 45 wetlands in three regions of Ontario. Twenty-two of these wetlands were located in the Muskoka-Haliburton Highlands and Temagami - North Bay regions and included bogs, fens, and marshes. Twenty-three were riverine marshes along the St. Lawrence River, near Cornwall, a Great Lakes area of concern, where Hg has been released through industrial activity. Overall, significant but weak negative relationships were found between pH and alkalinity of the surface waters and total water Hg concentrations (r2 = 0.28-0.30, p < 0.001). A significant positive relationship was found between dissolved organic C and total water Hg (r2 = 0.30). On average, St. Lawrence wetlands had lower total water Hg when compared with the inland wetlands. While a strong positive relationship was found between sediment organic matter and total sediment Hg concentrations (p < 0.001), the relationship was significantly different between the St. Lawrence and inland wetlands. In general, the St. Lawrence wetlands, despite the proximity to point sources of Hg, actually had lower sediment Hg, likely because of the lower organic matter. However, the St. Lawrence wetlands had twice the amount of Hg per unit of organic matter; the consequences of this difference for methyl mercury production and bioaccumulation need to be addressed.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".