Dynamic coupled metal transport-speciation model: Application to assess a zinc-contaminated lake
Bibliographic record
Abstract
A coupled metal transport and speciation/complexation model (TRANSPEC) has been developed to estimate the speciation and fate of multiple interconverting species in surface aquatic systems. Dynamic-TRANSPEC loosely, sequentially couples the speciation/complexation and fate modules that, for the unsteady state formulation, run alternatively at every time step. The speciation module first estimates species abundance using, in this version, MINEQL+ considering time-dependent changes in water and pore-water chemistry. The fate module is based on the quantitative water air sediment interaction (QWASI) model and fugacity/aquivalence formulation, with the option of using a pseudo-steady state solution to account for past discharges. Similarly to the QWASI model for organic contaminants, TRANSPEC assumes the instantaneous equilibrium distribution of metal species among dissolved, colloidal, and particulate phases based on ambient chemistry parameters that can be collected through conventional field methods. The model is illustrated with its application to Ross Lake (Manitoba, Canada) that has elevated Zn concentrations due to discharges over 70 years from a mining operation. Using measurements from field studies, the model reproduces year-round variations in Zn water concentrations. A 10-year projection for current conditions suggests decreasing Zn remobilization and export from the lake. Decreasing Zn loadings increases sediment-to-water transport but decreases water concentrations, and vice versa. Species distribution is affected by pH such that a decrease in pH increases metal export from the lake and vice versa.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".