The Role of Mercury Redox Reactions in Snow on Snow-to-Air Mercury Transfer
Bibliographic record
Abstract
Wet deposition of Hg in snow represents a major air-to-land flux of Hg in temperate and polar environments. However, the chemical speciation of Hg in snow and its chemical and physical behavior after deposition are poorly understood. To investigate Hg dynamics in snow, we followed Hg0 and total Hg concentrations in a snowpack above a frozen lake over 1 month. Our results indicate that newly deposited Hg is highly labile in snowpacks. On average, Hg levels in particular snow episodes decrease by 54% within 24 h after deposition. We hypothesize that Hg depletion in snow could be caused by a rapid snow-to-air Hg transfer resulting from Hg(II) photoinduced reduction to volatile Hg0. Both snowmelt incubated under a UV lamp at 17 degrees C and solid snow incubated under the sun at -10 degrees C in clear reaction vessels yielded a statistically significant increase in Hg0(aq) with time of exposure, while the Hg0(aq) levels remained constant in the dark controls. The snow-to-air Hg transfer we observed in this study suggests that the massive Hg deposition events observed in springtime in northern environments may have less impact than previously anticipated, since once deposited, Hg could be rapidly reduced and re-emitted.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".