Bioaccumulation and biomagnification of mercury in African lakes: The importance of trophic status
Bibliographic record
Abstract
Despite the global prevalence of both mercury (Hg) contamination and anthropogenic eutrophication, relatively little is known about the behavior of Hg in eutrophic and hypereutrophic systems or the effects of lake trophic status on Hg uptake and trophodynamics. In the current study we explore Hg trophodynamics at 8 tropical East African study sites ranging from mesotrophic to hypereutrophic, in order to assess the influence of lake trophic status on Hg uptake and biomagnification. Comprehensive water, plankton and fish samples were collected for analysis of total mercury (THg) and stable carbon and nitrogen isotopic ratios. We found evidence that uptake of THg into phytoplankton tended to be lower in higher productivity systems. THg concentrations in fish were generally low, and THg trophic magnification factors (TMFs; representing the average increase in contaminant concentrations from one trophic level to the next) ranged from 1.9 to 5.6. Furthermore TMFs were significantly lower in hypereutrophic lakes than in meso- and eutrophic lakes, and were negatively related to chlorophyll a concentrations both across our study lakes, and across African lakes for which literature data were available. These observations suggest that THg concentrations were strongly influenced by trophic status, with year-round high phytoplankton and fish growth rates reducing the potential for high THg in fish in these productive tropical 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".