A 14,000-year sediment record of mercury accumulation and isotopic signatures from Lake Malaya Chabyda (Siberia)
Bibliographic record
Abstract
Eurasian permafrost soils contain large amounts of organic carbon (OC) and mercury (Hg), sequestered by vegetation during past and present interglacial periods. Lake sediment archives may help understand past OC and Hg dynamics and how they interact with climate-related variables. We investigated Hg accumulation, OC dynamics, and Hg and OC stable isotopes in a 14,000 year sediment record from lake Malaya Chabyda (Central Yakutia, Russia). Sediment Hg was correlated to OC (p value <0.01), with lower OC and Hg accumulation rates (OCAR, HgAR) during the cold Younger Dryas (YD, 12,900–11,700 cal BP), when the lake level was low. Elevated sediment Δ200Hg (0.05 ± 0.11‰), representing dominant HgII deposition, and low δ13C, indicates low lake primary productivity during the YD. During the early Holocene, Δ200Hg and Δ199Hg decreased, while δ13C, δ202Hg, OCAR, and HgAR increased, suggesting enhanced algal primary productivity, in deeper, more turbid waters. From 4,100 cal BP to present, Hg/OC ratios and HgAR increased at constant OCAR, indicating an additional Hg source to the lake. Analysis of Hg isotopes suggests enhanced direct Hg0 uptake into lake waters, driven by primary production and efficient Hg burial. Our observations suggest that the gradual climate warming since the Last Glacial Termination and into the late Holocene led to enhanced OC and Hg burial in northern lakes and watersheds. Early Holocene enhanced Hg burial, but not OC, is possibly related to a renewed increase in lake primary productivity. Continued global warming may lead to further Hg sequestration in northern aquatic ecosystems.
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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 | 0.000 | 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".