Mercury transfer from watersheds to aquatic environments following the erosion of agrarian soils: A molecular biomarker approach
Bibliographic record
Abstract
Lake St. Pierre, an important freshwater location for sports and commercial fisheries in Canada, is composed of a 120 km2 stretch of the St. Lawrence River, located at the center of the St. Lawrence Lowlands. Receiving its waters from the St. François, Yamaska, Ottawa, and St. Lawrence Rivers, it is subjected to important inputs of mercury (Hg) and suspended particles eroded from its watershed. This study aims at tracing back the origin of terrigenous Hg loadings to Lake St. Pierre. The specific phenol signatures yielded by a mild oxidation of the terrestrial organic matter (TOM) carried in the water column was used as a tracer to identify the different sources of terrigenous Hg to the lake. Our results demonstrate that most of the Hg bound to suspended particulate matter (SPM-bound Hg) found in Lake St. Pierre is associated with TOM. We were also able to distinguish the relative influence that forested soils, mainly drained by the Ottawa River, and agrarian soils, located on nearby watersheds, exert on the lake's Hg burden. Our data strongly suggest that the erosion of vast areas of agrarian soils, drained by the Yamaska and St. François rivers to Lake St. Pierre, greatly facilitates the transfer of Hg from the watersheds to the lake. This study stresses the need to improve the management of agrarian soils and protect them from extensive erosion in order to preserve the integrity of the fish resources harvested in Lake St. Pierre. Key words: Mercury, soil erosion, Lake St. Pierre, lignin biomarkers
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".