Development of a mercury speciation, fate, and biotic uptake (BIOTRANSPEC) model: Application to Lahontan Reservoir (Nevada, USA)
Bibliographic record
Abstract
A mathematically linked mercury transport, speciation, kinetic, and simple biotic uptake (BIOTRANSPEC) model has been developed. An extension of the metal transport and speciation (TRANSPEC) model, BIOTRANSPEC estimates the fate and biotic uptake of inorganic (Hg(II)), elemental (Hg(0)) and organic (MeHg) forms of mercury and their species in the dissolved, colloidal (e.g., dissolved organic matter [DOM]), and particulate phases of surface aquatic systems. A pseudo-steady state version of the model was used to describe mercury dynamics in Lahontan Reservoir (near Carson City, NV, USA), where internal loading of the historically deposited mercury is remobilized, thereby maintaining elevated water concentrations. The Carson River is the main source of total mercury (THg), of which more than 90% is tightly bound in a gold-silver-mercury amalgam, to the system through loadings in the spring, with negligible input from the atmospheric deposition. The speciation results suggest that aqueous species are dominated by Hg-DOM, Hg(OH)(2), and HgClOH. Sediment-to-water diffusion of MeHg and Hg-DOM accounts for approximately 10% of total loadings to the water column. The water column acts as a net sink for MeHg by reducing its levels through two competitive processes: Uptake by fish, and net MeHg demethylation. Although reservoir sediments produce significant amounts of MeHg (4 g/d), its transport from sediment to water is limited (1.6 g/d), possibly because of its adsorption on metal oxides of iron and manganese at the sediment-water interface. Fish accumulate approximately 45% of the total MeHg mass in the water column, and 9% of total MeHg uptake by fish leaves the system because of fishing. Results from this new model reiterate the previous conclusion that more than 90% of THg input is retained in sediment, which perpetuates elevated water concentrations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".