Algal scavenging of mercury in preindustrial Arctic lakes
Bibliographic record
Abstract
Abstract The geochemical speciation of total mercury (THg) was examined in pre‐1800 Arctic lake sediments to improve understanding of the factors controlling “natural baseline” THg. Solid‐phase binding forms of THg were determined by sequential extraction of dated cores from three lakes in different ecozones (barren tundra, grassy tundra, and boreal forest). Sediment organic matter (OM) was mostly of algal origin. Mercury was highly concentrated in the sediment OM fraction (OM‐Hg), comprising 60–87% of THg, while OM (as total organic carbon) constituted only 0.6–13% of sediment dry weight (DW). OM‐Hg concentrations were equivalent to 159 ± 13 to 776 ± 215 ng Hg g−1 DW in algal OM and were enriched 2–39 times compared to sediment THg, indicating that even small changes in algal OM inputs could significantly alter THg. OM‐Hg explained 76–96% of the variation in THg concentrations over many centuries. Concentrations of S2 carbon (an algal productivity proxy) and OM‐Hg were significantly correlated in two lakes but not in the boreal forest lake possibly because of OM remineralization in its deep water column. Fluxes of S2 carbon, OM‐Hg, and THg were highly correlated in the barren tundra lake but could not be calculated for the other lakes. The results overall indicate that high algal Hg concentrations due to scavenging of available Hg controlled OM‐Hg flux to sediments, thus driving changes in THg concentrations and fluxes. These findings improve our understanding of the long‐term stability of baseline THg in northern lakes under a changing climate, including in the modern era.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".