Mercury in the Canadian Arctic Terrestrial Environment: An Update
Bibliographic record
Abstract
Contaminants in the Canadian Arctic have been studied over the last twenty years under the guidance of the Northern Contaminants Program. This paper provides the current state of knowledge on mercury (Hg) in the Canadian Arctic terrestrial environment. Snow, ice, and soils on land are key reservoirs for atmospheric deposition and can become sources of Hg through the melting of terrestrial ice and snow and via soil erosion. In the Canadian Arctic, new data have been collected for snow and ice that provide more information on the net accumulation and storage of Hg in the cryosphere. Concentrations of total Hg (THg) in terrestrial snow are highly variable but on average, relatively low (<5 ng L(-1)), and methylmercury (MeHg) levels in terrestrial snow are also generally low (<0.1 ng L(-1)). On average, THg concentrations in snow on Canadian Arctic glaciers are much lower than those reported on terrestrial lowlands or sea ice. Hg in snow may be affected by photochemical exchanges with the atmosphere mediated by marine aerosols and halogens, and by post-depositional redistribution within the snow pack. Regional accumulation rates of THg in Canadian Arctic glaciers varied little during the past century but show evidence of an increasing north-to-south gradient. Temporal trends of THg in glacier cores indicate an abrupt increase in the early 1990 s, possibly due to volcanic emissions, followed by more stable, but relatively elevated levels. Little information is available on Hg concentrations and processes in Arctic soils. Terrestrial Arctic wildlife typically have low levels of THg (<5 μg g(-1) dry weight) in their tissues, although caribou (Rangifer tarandus) can have higher Hg because they consume large amounts of lichen. THg concentrations in the Yukon's Porcupine caribou herd vary among years but there has been no significant increase or decrease over the last two decades.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".