Atmospheric Transport of Mercury to the Tibetan Plateau
Bibliographic record
Abstract
The Tibetan Plateau (including the Himalayas) is one of the most remote and cold regions in the world. It has very limited to nonexistent industry but is adjacent to the two most populous and rapidly industrializing countries (China and India) and thus provides a unique location for studying the atmospheric transport of mercury. Here we report the first study on the atmospheric transport of mercury to the Tibetan Plateau. The total mercury profiles in four snowpits from glaciers above 5700 m asl along a southwest-northeast transect across the central Tibetan Plateau were obtained in 2005-2006. In general, the total mercury concentrations in the snow samples ranged from < 1 to 9 ng L(-1), increasing northeastward from the southernmost site at Mount Everest. Higher total mercury concentrations were found in the snow deposited in the nonmonsoon season, as indicated by seasonal variation of delta18O values and major ions in the snowpack. The annual atmospheric depositional flux of total Hg was estimated to range from 0.74 to 2.97 microg m(-2) yr(-1) in the region, the majority of which occurred via particulate matter deposition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".