Evaluating sources of mercury in Canada's Mackenzie River
Bibliographic record
Abstract
Arctic rivers may be the largest net sources of mercury (Hg) to the Arctic Ocean, yet riverine sources of Hg remain poorly characterized compared to atmospheric processes. This article reviews the current state of knowledge on Hg inputs to the Mackenzie River and Valley in Northern Canada from six point and non-point sources. Point sources include the locations of mines, fossil fuel extraction facilities, and retrogressive permafrost thaw slumps. Non-point sources are assessed through models of Hg release from anthropogenic and wildfire-derived atmospheric Hg deposition (GEM-MACH-Hg), permafrost thaw (SiBCASA), and rainfall-induced soil erosion (RUSLE). Ongoing anthropogenic activity is likely a minor contributor to Hg levels in the Mackenzie Valley as production from the fossil fuel and mining industries have steadily declined over the past two decades. Conversely, Hg inputs from atmospheric deposition, permafrost thaw, and permafrost thaw slumps have increased due to climate change and the re-emission of legacy Hg. The widespread influence of atmospheric Hg deposition makes it the dominant source of Hg to both aquatic and terrestrial systems in the Mackenzie Valley, although soil erosion inputs, while higher, are restricted to regions of steep terrain. Climate-driven increases in terrestrial Hg release, particularly from permafrost degradation and erosion, are emerging as key localized drivers of Hg inputs in the Mackenzie Valley.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 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".