Atmospheric Mercury Depositional Chronology Reconstructed from Lake Sediments and Ice Core in the Himalayas and Tibetan Plateau
Bibliographic record
Abstract
Alpine lake sediments and glacier ice cores retrieved from high mountain regions can provide long-term records of atmospheric deposition of anthropogenic contaminants such as mercury (Hg). In this study, eight lake sediment cores and one glacier ice core were collected from high elevations across the Himalaya-Tibet region to investigate the chronology of atmospheric Hg deposition. Consistent with modeling results, the sediment core records showed higher Hg accumulation rates in the southern slopes of the Himalayas than those in the northern slopes in the recent decades (post-World War II). Despite much lower Hg accumulation rates obtained from the glacier ice core, the temporal trend in the Hg accumulation rates matched very well with that observed from the sediment cores. The combination of the lake sediments and glacier ice core allowed us to reconstruct the longest, high-resolution atmospheric Hg deposition chronology in High Asia. The chronology showed that the Hg deposition rate was low between the 1500s and early 1800, rising at the onset of the Industrial Revolution, followed by a dramatic increase after World War II. The increasing trend continues to the present-day in most of the records, reflecting the continuous increase in anthropogenic Hg emissions from South Asia.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".