Sources of riverine mercury across the Mackenzie River Basin; inferences from a combined Hg C isotopes and optical properties approach
Bibliographic record
Abstract
The Arctic environment harbors a complex mosaic of mercury (Hg) and carbon (C) reservoirs, some of which are rapidly destabilizing in response to climate warming. The sources of riverine Hg across the Mackenzie River basin (MRB) are uncertain, which leads to a poor understanding of potential future release. Measurements of dissolved and particulate mercury (DHg, PHg) and carbon (DOC, POC) concentration were performed, along with analyses of Hg stable isotope ratios (incl. ∆199Hg, δ202Hg), radiocarbon content (∆14C) and optical properties of DOC of river water. Isotopic ratios of Hg revealed a closer association to terrestrial Hg reservoirs for the particulate fraction, while the dissolved fraction was more closely associated with atmospheric deposition sources of shorter turnover time. There was a positive correlation between the ∆14C-OC and riverine Hg concentration for both particulate and dissolved fractions, indicating that waters transporting older-OC (14C-depleted) also contained higher levels of Hg. In the dissolved fraction, older DOC was also associated with higher molecular weight, aromaticity and humic content, which are likely associated with higher Hg-binding potential. Riverine PHg concentration increased with turbidity and SO4 concentration. There were large contrasts in Hg concentration and OC age and quality among the mountain and lowland sectors of the MRB, which likely reflect the spatial distribution of various terrestrial Hg and OC reservoirs, including weathering of sulfate minerals, erosion and extraction of coal deposits, thawing permafrost, forest fires, peatlands, and forests. Results revealed major differences in the sources of particulate and dissolved riverine Hg, but nonetheless a common positive association with older riverine OC. These findings reveal that a complex mixture of Hg sources, supplied across the MRB, will contribute to future trends in Hg export to the Arctic Ocean under rapid environmental changes.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".