Distribution and Fluxes of Total and Methylmercury in Lake Superior
Bibliographic record
Abstract
Despite the importance and size of Lake Superior, little is known regarding the biogeochemical cycling or distribution of mercury within its waters. We present the results from two research cruises on total Hg (HgT) and methylmercury (MeHg) distributions in aqueous and particulate phases, and in offshore sediments. Open waters of Lake Superior are similar in HgT content to Lakes Michigan and Ontario (sub-ng L(-1)), whereas MeHg was only 1% of HgT. Seasonality in aqueous HgT distribution was observed, most likely from tributary inputs during Spring snowmelt. Suspended particles were enriched in MeHg relative to water and surficial sediments, suggesting enhanced particle partitioning followed by demethylation in the water column and in surface sediments. Distribution coefficients for mercury in surficial sediments were lower than those in suspended material, likely due to remineralization. Preliminary estimates of mass balance indicate that air-water exchange processes such as evasion and wet deposition dominate the HgT budget, due to the basin's relatively small watershed area relative to lake area. In contrast, methylmercury cycling within Lake Superior is influenced more strongly by watershed sources, as well as by sedimentary sources and photodemethylation. The Hg cycle in Lake Superior is unique in that it is more similar in many aspects to that in marine systems than in small lakes, where management data for freshwaters typically originates.
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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".