A Holocene Perspective on Algal Mercury Scavenging to Sediments of an Arctic Lake
Bibliographic record
Abstract
ABSTRACT: Anthropogenic activities have increased the amount of mercury (Hg) transported atmospherically to the Arctic. At the same time, recent climate warming is altering the limnology of arctic lakes and ponds, including increases in aquatic primary production. It has been hypothesized that climate-driven increases in aquatic production have enhanced Hg scavenging from the water column, and that this mechanism may account for much of the recent rise in lake sediment Hg. Here, we test the relationship between climate, algal production, and sediment Hg using a well-dated and multiproxy lake sediment record spanning the Holocene from Lake CF3 (Baffin Island, Nunavut, Canada). During the early Holocene, peak (summer) insolation drove July air temperatures higher than present, and resulted in increased autochthonous primary production as recorded by total organic matter, spectrally inferred Chl-a, diatom abundance, and carbon stable isotopic signatures. However, there are no relationships between any of these proxies and sediment Hg concentrations during this interval. Given that the behavior of preindustrial Hg was relatively stable during past intervals of naturally mediated high production, we surmise that postindustrial increases in Hg accumulation within CF3 reflect a multiplicative effect of atmospheric deposition of anthropogenic Hg and increased sedimentation rates.
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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) | 0.002 | 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".