Experimental Evidence of a Linear Relationship between Inorganic Mercury Loading and Methylmercury Accumulation by Aquatic Biota
Bibliographic record
Abstract
Developing effective regulations on mercury (Hg) emissions requires a better understanding of how atmospheric Hg deposition affects methylmercury (MeHg) levels in aquatic biota. This study tested the hypothesis that MeHg accumulation in aquatic food webs is related to atmospheric Hg deposition. We simulated a range of inorganic Hg deposition rates by adding isotopically enriched Hg(II) (90.9% 202Hg) to 10-m diameter mesocosms in a boreal lake. Concentrations of experimentally added ("spike") Hg were monitored in zooplankton, benthic invertebrates, and fish. Some Hg(II) added to the mesocosms was methylated and incorporated into the food web within weeks, demonstrating that Hg(II) deposited directly to aquatic ecosystems can become quickly available to biota. Relationships between Hg(II) loading rates and spike MeHg concentrations in zooplankton, benthic invertebrates, and fish were linear and significant. Furthermore, spike MeHg concentrations in the food web were directly proportional to Hg(II) loading rates (i.e., a percent change in Hg(II) loading rate resulted in, statistically, the same percent change in MeHg concentration). This is the first experimental determination of the relationship between Hg(II) loading and MeHg bioaccumulation in aquatic biota. We conclude that changes in atmospheric Hg deposition caused by increases or decreases in Hg emissions will ultimately affect MeHg levels in aquatic food webs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".