Concentrations and Yields of Total Hg and MeHg in Large Boreal Rivers Linked to Water and Wetland Coverage in the Watersheds
Bibliographic record
Abstract
Large rivers are major contributors of mercury (Hg) exports to the ocean, as they integrate processes of loading and loss occurring at the watershed level. Within a watershed, stream-scale studies have revealed that specific landscape features, such as wetlands or lakes, are hotspots for Hg and methylmercury (MeHg) loading, sinks and transformation, but we still do not know how these landscape features operate at the whole network scale and over large geographic gradients. In this study, we evaluate how landscape metrics (vegetation types, wetland and lake cover, climate, hydrology) are related to riverine concentrations and watershed yields of Hg and MeHg in 18 large boreal rivers draining watersheds that range from 44 km2 to 209 453 km2, distributed along a 650 km latitudinal transect in the James Bay region of Québec. Our results reinforce the role of wetlands as sources of MeHg, but further show that surface coverage of water in the watershed is the major driver of both Hg and MeHg concentrations and exports to the coast at the whole network scale. Our findings also demonstrate that seasonality modulates the relationship between landscape features and the various Hg forms. Based on hydrometric data, we additionally estimate annual exports for the whole Eastern James Bay to 441 kg Hg and 15 kg MeHg, for an average landscape yield of 1.24 g Hg km-2 y-1 and 0.041 g MeHg km-2 y-1. Our study provides tools to broadly predict riverine Hg concentrations and exports with only a few easily accessible landscape metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".