Diminishing Mercury Bioaccumulation in Zooplankton along an Estuarine Salinity Gradient
Bibliographic record
Abstract
Estuarine transition zones (ETZs) are biogeochemically complex, nutrient-rich environments supporting diverse and productive food webs. They may also be sites of microbial production of methylmercury (MeHg) and bioaccumulation of this neurotoxicant at the base of the food web. However, the environmental drivers controlling these mechanisms are unclear. We studied the pattern of MeHg bioaccumulation in zooplankton along the 200 km ETZ of a large North American estuary, the St. Lawrence Estuary (Québec, Canada). Our approach integrated the dynamic variation in ETZ geochemistry, focusing on MeHg speciation change, alongside ecological factors, including community composition and stable isotopic tracers of diet and habitat. MeHg bioaccumulation decreased with distance downstream along the ETZ, driven by the salinity gradient and traced by the isotopic signature of sulfur in zooplankton. MeHg speciation modeling suggested that complexation to dissolved organic matter may be used as a proxy of the trophic transfer of MeHg to zooplankton. Further, the binding of MeHg to organic matter was reduced by the presence of chloride, thus reducing the trophic transfer of MeHg. We propose a conceptual model for MeHg cycling in ETZ of large estuaries that hypothesizes that higher-level consumers in turbid upstream regions may face heightened risks of MeHg toxicity but that Hg levels diminish drastically as salinity increases.
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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 | 0.000 |
| 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".