Evidence for Control of Mercury Accumulation Rates in Canadian High Arctic Lake Sediments by Variations of Aquatic Primary Productivity
Bibliographic record
Abstract
Climate warming in the 20th Century has had profound effects on the limnology of High Arctic lakes, including substantial increases in autochthonous primary productivity (APP). Here, we report organic carbon and Hg core profiles from two lakes which support the hypothesis that 20th Century increases in sedimentary Hg at these latitudes were largely driven by APP increases, via Hg scavenging by algae and/or suspended detrital algal matter. Hydrocarbons quantitatively released by thermal cracking of algal-derived organic matter ("S2" carbon) were used to reconstruct past APP. Variation of S2 flux accounted for 87-91% of the variance in total Hg flux in the study lakes since 1854. Mercury and S2 carbon were also associated during the pre-Industrial Period, co-varying by as much as 30% during past warm/cool periods. As a test of the hypothesis, predicted values for 20th Century [Hg] were derived from pre-1900 Hg-S2 relationships. Measured 20th Century [Hg] was on average only 6-11% higher than that predicted in one lake, and 33% higher in the other. S2-normalization of [Hg] in the latter lake suggested that 78% of the average increase in 20th Century [Hg] could be explained by scavenging. These findings suggest that the atmospheric contribution of long-range anthropogenic Hg to High Arctic lakes may have been overestimated by several-fold because of this climate-driven process, and was responsible for no more than 22% of the 20th Century [Hg] increase in the study lakes.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".