Unprecedented Increases in Total and Methyl Mercury Concentrations Downstream of Retrogressive Thaw Slumps in the Western Canadian Arctic
Bibliographic record
Abstract
Retrogressive thaw slumps (RTSs) are thermokarst features created by the rapid thaw of ice-rich permafrost, and can mobilize vast quantities of sediments and solutes downstream. However, the effect of slumping on downstream concentrations and yields of total mercury (THg) and methylmercury (MeHg) is unknown. Fluvial concentrations of THg and MeHg downstream of RTSs on the Peel Plateau (Northwest Territories, Canada) were up to 2 orders of magnitude higher than upstream, reaching concentrations of 1,270 ng L –1 and 7 ng L –1, respectively, the highest ever measured in uncontaminated sites in Canada. MeHg concentrations were particularly elevated at sites downstream of RTSs where debris tongues dammed streams to form reservoirs where microbial Hg methylation was likely enhanced. However, > 95% of the Hg downstream was typically particle-bound and potentially not readily bioavailable. Mean open-water season yields of THg (610 mg km –2 d –1 ) and MeHg (2.61 mg km –2 d –1 ) downstream of RTSs were up to an order of magnitude higher than those for the nearby large Yukon, Mackenzie and Peel rivers. We estimate that ∼5% of the Hg stored for centuries or millennia in northern permafrost soils (88 Gg) is susceptible to release into modern-day Hg biogeochemical cycling from further climate changes and thermokarst formation.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".