Photoreducible Mercury Loss from Arctic Snow Is Influenced by Temperature and Snow Age
Bibliographic record
Abstract
Mercury (Hg) is an important environmental contaminant, due to its neurotoxicity and ability to bioaccumulate. The Arctic is a mercury-sensitive region, where organisms can accumulate high Hg concentrations. Snowpack mercury photoredox reactions may control how much Hg is transported with melting Arctic snow. This work aimed to (1) determine the significance of temperature combined with UV irradiation intensity and snow age on Hg(0) flux from Arctic snow and (2) elucidate the effect of temperature on snowpack Hg photoreduction kinetics. Using a Teflon flux chamber, snow temperature, UV irradiation, and snow age were found to significantly influence Hg(0) flux from Arctic snow. Cross-correlation analysis results suggest that UV radiation has a direct effect on Hg(0)flux, while temperature may indirectly influence flux. Laboratory experiments determined that temperature influenced Hg photoreduction kinetics when snow approached the melting point (>-2 °C), where the pseudo-first-order reduction rate constant, k, decreased twofold, and the photoreduced Hg amount, Hg(II)red, increased 10-fold. This suggests that temperature influences Hg photoreduction kinetics indirectly, likely by altering the solid:liquid water ratio. These results imply that large mass transfers of Hg from snow to air may take place during the Arctic snowmelt period, altering photoreducible Hg retention and transport with snow meltwater.
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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) | 0.000 | 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".