Mercury fate in ageing and melting snow: Development and testing of a controlled laboratory system
Bibliographic record
Abstract
A snow cover can modify when, to what extent, and in what form atmospherically deposited mercury is released to the underlying surface media and/or back to the atmosphere. Investigations of mercury transport and transformation processes in snow packs are hampered by the difficulty in controlling experimental and melt conditions and due to the huge variability in the composition and physical structure of environmental snow packs. A method was developed that allows the detailed mechanistic investigation of mercury fate in snow that is made, aged and melted under controlled laboratory conditions. A number of control samples established that mercury in indoor air, scavenged during the snow making process, constitutes the dominant source of mercury in the artificial snow. No addition of mercury is required. The amount of mercury in fresh snow was quantitatively (102 and 106% in two experiments) recovered in the dissolved and particulate fractions of the melt water and the vessel head space, confirming a mass balance for mercury and the absence of unquantifiable mercury sources and sinks in the experimental system. In snow made from unmodified tap water, more than half of the mercury present in the snowpack was recovered from the bottom of the snow vessel after all of the snow had melted. Such late elution is indicative of mercury being mostly associated with particles that are filtered by, and retained in, the shrinking snowpack. Addition of salt to the snow-making water at an environmentally realistic pH notably shifted the distribution of mercury in the snowpack from the particulate to the dissolved phase, resulting in more than 60% of the mercury eluting in the dissolved phase of early melt water fractions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".