Mobilization and Transformation of Mercury Across a Dammed Boreal River Are Linked to Carbon Processing and Hydrology
Bibliographic record
Abstract
Abstract Reservoirs are known to accelerate the mobilization and cycling of mercury and carbon as a result of flooding of terrestrial organic matter, which can lead to environmental concerns at local and broader spatial scales. We explored the covariation of mercury (Hg) and carbon (C) functional pools in natural and recently dammed portions of the aquatic network of the Romaine River watershed in Northern Quebec, Canada, to understand how the fate of these elements varies across systems with contrasting hydrology and environmental conditions. We found that total Hg (THg) concentrations in surface waters were relatively constant along the network, whereas both the concentrations and proportions of MeHg tended to increase in reservoirs compared to surrounding nonflooded systems, and along the cascade of reservoirs. Whereas THg was related to total and terrestrial pools of dissolved organic carbon (DOC), MeHg was weakly related to DOC but strongly linked to surface concentrations of CO2, as well as to concentrations of iron and manganese. The latter are proxies of cumulative organic matter processing within the network, presumably in anoxic portions of shallow bays, deep reservoir waters, and river sediments, as well as in prior seasons (e.g., under ice). Our results suggest that these deep boreal reservoirs acted more as transformation sites for Hg that was already present than as mobilizers of new Hg, and that under ice metabolism plays a role in MeHg production in these systems as we found strong dichotomies in MeHg patterns between spring and summer.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".