Interactions between mercury and phytoplankton: Speciation, bioavailability, and internal handling
Bibliographic record
Abstract
The present review describes and discusses key interactions between mercury (Hg) and phytoplankton to highlight the role of phytoplankton in the biogeochemical cycle of Hg and to understand direct or indirect Hg effects on phytoplankton. Phytoplankton are exposed to various Hg species in surface waters. Through Hg uptake, phytoplankton affect the concentration, speciation, and fate of Hg in aquatic systems. The mechanisms by which phytoplankton take up Hg are still not well known, but several studies have suggested that both facilitated transport and passive diffusion could be involved. Once internalized, Hg will impact several physiological processes, including photosynthesis. To counteract these negative effects, phytoplankton have developed several detoxification strategies, such as the reduction of Hg to elemental Hg or its sequestration by intracellular ligands. Based on the toxicological studies performed so far in the laboratory, Hg is unlikely to be toxic to phytoplankton when they are exposed to environmentally relevant Hg concentrations. However, this statement should be taken with caution because questions remain as to which Hg species control Hg bioavailability and about Hg uptake mechanisms. Finally, phytoplankton are primary producers, and accumulated Hg will be transferred to higher consumers. Phytoplankton are a key component in aquatic systems, and their interactions with Hg need to be further studied to fully comprehend the biogeochemical cycle of Hg and the impact of this ubiquitous metal on ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".