Hydrology and Seasonality Shape the Coupling of Dissolved Hg and Methyl‐Hg With DOC in Boreal Rivers in Northern Québec
Bibliographic record
Abstract
Abstract Co‐loading of mercury (Hg) with dissolved organic carbon (DOC) is a key driver of the observed spatial and temporal Hg patterns among aquatic ecosystems. Their strong biogeochemical coupling has spurred the use of DOC as a predictor of Hg concentrations and exports in boreal regions where sampling logistics for Hg are costly and complex. Yet relationships between Hg and methylmercury (MeHg) with DOC have recently been shown to be highly variable in terms of slope and strength, suggesting that mechanisms other than co‐transport along the land‐water continuum may drive the relationship between Hg and DOC across landscapes. In this study, we explore the relationship between Hg and MeHg with DOC across 18 boreal rivers collectively draining over 350,000 km2 of the eastern James Bay territory (Québec), comprising watersheds with a wide range of vegetation, water residence time and riverine DOC concentrations and optical properties. Our results show that although a large portion of the variation in Hg and MeHg is explained by concentrations of DOC, Hg‐DOC and MeHg‐DOC relationships and ratios vary greatly both spatially and temporally. We show that ratios and strength of the coupling can be predicted from system hydrology, with declines in Hg:DOC and increase in MeHg:DOC ratios and stronger coupling during the seasonal progression to warmer temperatures, higher water evaporation, and longer residence time. Our study highlights the role of seasonal hydrology and biogeochemical processing in governing Hg, MeHg and DOC patterns in boreal rivers.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".