A Model for Methylmercury Uptake and Trophic Transfer by Marine Plankton
Bibliographic record
Abstract
Methylmercury (MeHg) concentrations can increase by 100 000 times between seawater and marine phytoplankton, but levels vary across sites. To better understand how ecosystem properties affect variability in planktonic MeHg concentrations, we develop a model for MeHg uptake and trophic transfer at the base of marine food webs. The model successfully reproduces measured concentrations in phytoplankton and zooplankton across diverse sites from the Northwest Atlantic Ocean. Highest MeHg concentrations in phytoplankton are simulated under low dissolved organic carbon (DOC) concentrations and ultraoligotrophic conditions typical of open ocean regions. This occurs because large organic complexes bound to MeHg inhibit cellular uptake and cell surface area to volume ratios are greatest under low productivity conditions. Modeled bioaccumulation factors for phytoplankton (10 2.4 –10 5.9 ) are more variable than those for zooplankton (10 4.6 –10 6.2 ) across ranges in DOC (40–500 μM) and productivities (ultraoligotrophic to hypereutrophic) typically found in marine ecosystems. Zooplankton growth dilutes their MeHg body burden, but they also consume greater quantities of MeHg enriched prey at larger sizes. These competing processes lead to lower variability in MeHg concentrations in zooplankton compared to phytoplankton. Even under hypereutrophic conditions, modeled growth dilution in marine zooplankton is insufficient to lower their MeHg concentrations, contrasting findings from freshwater ecosystems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".