Influence of plankton mercury dynamics and trophic pathways on mercury concentrations of top predator fish of a mining-impacted reservoir
Bibliographic record
Abstract
Physical and biogeochemical characteristics of the aquatic environment that affect growth dynamics of phytoplankton and the zooplankton communities that depend on them may also affect uptake of methylmercury (MeHg) into the pelagic food web of oligotrophic reservoirs. We evaluated changes in the quality and quantity of suspended particulate material, zooplankton taxonomy, and MeHg concentrations coincident with seasonal changes in water storage of a mining-impacted reservoir in northern California, USA. MeHg concentrations in bulk zooplankton increased from 4 ng·g–1at low water to 77 ± 6.1 ng·g–1at high water and were positively correlated with cladoceran biomass (r = 0.66) and negatively correlated with rotifer biomass (r = –0.65). Stable isotope analysis revealed overall higher MeHg concentrations in the pelagic-based food web relative to the benthic-based food web. Statistically similar patterns of trophic enrichment of MeHg (slopes) for the pelagic and benthic food webs and slightly higher MeHg concentrations in zooplankton than in benthic invertebrates suggest that the difference in MeHg bioaccumulation among trophic pathways is set at the base of the food webs. These results suggest an important role for plankton dynamics in driving the MeHg content of zooplankton and ultimately MeHg bioaccumulation in top predators in pelagic-based 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 | 0.000 |
| 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".