No evidence for large mercury release from the Greenland Ice Sheet: recent conclusions on global mercury budget invalidated. 
Bibliographic record
Abstract
Local glaciers and ice caps in the Northern Hemisphere and the Greenland Ice Sheet (GrIS) have recently been suggested to be important parts of the Arctic mercury (Hg) budget. Based on ice-core data, the recently estimated total glacial Hg pool is approximately 2,400 tons, approximately 97% of which is in Greenland1. With Hg concentrations in pre-industrial ice being significantly lower than in ice formed during the 19th and 20th centuries, the dominating source of glacial Hg and input to the Arctic Hg budget is ascribed to long-range transport of anthropogenic Hg emissions2.Alarming concentrations of Hg in meltwater from the western margin of the GrIS were recently reported3. With Hg concentrations reported as being 100 - 1000 times higher than what is known for other freshwater systems of Greenland and a postulated increase in Hg export to downstream environment following climate warming and asserted global importance of Hg fluxes from the GrIS, these extraordinary concentrations and conclusions calls for independent verification.In our current study4, we expand the sampling of subglacial meltwater from 21 representative outlets at GrIS in 2021 and 2022 to get a better scientific basis for conclusions on the magnitude of glacial Hg sources in Greenland. Results from our study consistently show that both total and dissolved Hg concentrations in glacial meltwater over space and time are very low (generally
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".