Mercury contamination in aquatic ecosystems under a changing environment: Implications for the Three Gorges Reservoir
Bibliographic record
Abstract
Mercury is one of the primary contaminants of global concern. As anthropogenic emissions of mercury are gradually placed under control, evidence is emerging that biotic mercury levels in many aquatic ecosystems are increasingly driven by internal biogeochemical processes, especially in ecosystems that have been undergoing dramatic environmental changes. Here we review the unique properties of mercury that are responsible for the exceptional sensitivity of its biogeochemical cycles to changes in climatic, geochemical, biological and ecological processes. We show that, due to rapid climate warming, a shift from sources-driven to processes-driven mercury bioaccumulation is already happening in the Arctic marine ecosystem. We further suggest that such a shift might also be operating in the Three Gorges Reservoir due to changes in these biogeochemical processes induced by the damming. As a result, the effectiveness of mercury emission control is expected to be followed by long delays before ensuing reduction is seen in food-web levels, making it all the more pressing to control and reduce mercury emissions to the reservoir. Long-term monitoring and targeted studies are urgently needed to understand how biotic mercury levels in the reservoir are responding to changes in mercury emissions and in biogeochemical processes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".