A 300-Year High-Resolution Greenland Ice Record of Large-Scale Atmospheric Pollution by Arsenic in the Northern Hemisphere
Bibliographic record
Abstract
We report the first high-resolution record of arsenic (As) observed in Greenland snow and ice for the periods 1711-1970 and 2003-2009 AD. The results show well-defined large-scale atmospheric pollution by this toxic element in the northern hemisphere, beginning as early as the 18th century. The most striking feature is an abrupt, unprecedented enrichment factor (EF) peak in the late 1890s, with an ∼30-fold increase in the mean value above the Holocene natural level. Highly enriched As was evident until the late 1910s; a sharp decline was observed after the First World War, reaching a minimum in the early 1930s during the Great Depression. A subsequent increase lasted until the mid-1950s, before decreasing again. Comparisons between the observed variations and Cu smelting data indicate that Cu smelting in Europe and North America was the likely source of early anthropogenic As in Greenland. Despite a significant reduction of ∼80% in concentration and ∼60% in EF from the 1950s to the 2000s, more than 80% of present-day As in Greenland is of anthropogenic origin, probably due to increasing As emissions from coal combustion in China. This highlights the demand for the implementation of national and international environmental regulations to further reduce As emissions.
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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 | 0.001 |
| 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".