Fate of inorganic mercury and methyl mercury within the snow cover in the low arctic tundra on the shore of Hudson Bay (Québec, Canada)
Bibliographic record
Abstract
Snow samples were collected in the seasonal snow cover of the low arctic tundra (Whapmagoostui‐Kuujjuarapik, Québec) during episodic atmospheric mercury depletion events (AMDEs) and in the snowmelt period, long after AMDEs had occurred. Total and methyl mercury analyses were done in order to investigate the critical factors influencing the fate of mercury once deposited in the snowpack. Following AMDEs, snow total mercury (THg) concentrations increased and were inversely proportional to the distance from Hudson Bay. The correlations between MeHg, sulfate (SO42−), and chlorine (Cl−) snow concentrations implicated marine aerosols as a significant source of MeHg, independent of AMDEs. However, the newly deposited MeHg was unstable in the snow cover as 15–56% of the MeHg was demethylated or otherwise “lost” during the nighttime period. In contrast, during the snowmelt period, marine aerosols were not a significant source of MeHg. MeHg snow concentrations higher than 200 pg L−1 were observed when snow's heterotrophic plate counts, total suspended volatile solids, and total suspended solids were higher than 5.0 × 105 CFU L−1, 25 mg L−1, and 90 mg L−1, respectively. During the snowmelt, although the THg snow concentrations remained at 8–9 ng L−1, the proportion of MeHg increased from 2.7 to 7.6%. This is the first report suggestive of the presence of mercury methylation activities within the snow cover of the low arctic tundra.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".