Response of a Macrotidal Estuary to Changes in Anthropogenic Mercury Loading between 1850 and 2000
Bibliographic record
Abstract
Methylmercury (MeHg) bioaccumulation in marine food webs poses risks to fish-consuming populations and wildlife. Here we develop and test an estuarine mercury cycling model for a coastal embayment of the Bay of Fundy, Canada. Mass budget calculations reveal that MeHg fluxes into sediments from settling solids exceed losses from sediment-to-water diffusion and resuspension. Although measured methylation rates in benthic sediments are high, rapid demethylation results in negligible net in situ production of MeHg. These results suggest that inflowing fluvial and tidal waters, rather than coastal sediments, are the dominant MeHg sources for pelagic marine food webs in this region. Model simulations show water column MeHg concentrations peaked in the 1960s and declined by almost 40% by the year 2000. Water column MeHg concentrations respond rapidly to changes in mercury inputs, reaching 95% of steady state in approximately 2 months. Thus, MeHg concentrations in pelagic organisms can be expected to respond rapidly to mercury loading reductions achieved through regulatory controls. In contrast, MeHg concentrations in sediments have steadily increased since the onset of industrialization despite recent decreases in total mercury loading. Benthic food web MeHg concentrations are likely to continue to increase over the next several decades at present-day mercury emissions levels because the deep active sediment layer in this system contains a large amount of legacy mercury and requires hundreds of years to reach steady state with inputs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".