Mercury loading within the Selenga River basin and Lake Baikal, Siberia
Bibliographic record
Abstract
Mercury (Hg) loading in Lake Baikal, a UNESCO world heritage site, is growing and poses a serious health concern to the lake's ecosystem due to the ability of Hg to transform into a toxic form, known as methylmercury (MeHg). Monitoring of Hg into Lake Baikal is spatially and temporally sparse, highlighting the need for insights into historic Hg loading. This study reports measurements of Hg concentrations from water collected in August 2013 and 2014 from across Lake Baikal and its main inflow, the Selenga River basin (Russia, Mongolia). We also report historic Hg contamination using sediment cores taken from the south and north basins of Lake Baikal, and a shallow lake in the Selenga Delta. Field measurements from August 2013 and 2014 show high Hg concentrations in the Selenga Delta and river waters, in comparison to pelagic lake waters. Sediment cores from Lake Baikal show that Hg enrichment commenced first in the south basin in the late-19th century, and then in the north basin in the mid-20th century. Hg flux was also 20-fold greater in the south basin compared to the north basin sediments. Hg enrichment was greatest in the Selenga Delta shallow lake (Enrichment Ratio (ER) = 2.3 in 1994 CE), with enrichment occurring in the mid-to late-20th century. Local sources of Hg are predominantly from gold mining along the Selenga River, which have been expanding over the last few decades. More recently, another source is atmospheric deposition from industrial activity in Asia, due to rapid economic growth across the region since the 1980s. As Hg can bioaccumulate and biomagnify through trophic levels to Baikal's top consumer, the world's only truly freshwater seal (Pusa sibirica), it is vital that Hg input at Lake Baikal and within its catchment is monitored and controlled.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".