Development of a coupled metal speciation-fate model for surface aquatic systems
Bibliographic record
Abstract
A coupled metal transport and speciation model (TRANSPEC) has been developed for surface aquatic systems that explicitly considers the influence of metal speciation on fate. The TRANSPEC, which is general to most metal and surface aquatic systems, is constructed by sequentially coupling the speciation/complexation module (in this application MINEQL+) with the fugacity/aquivalence approach for the fate calculations. This model formulation increases the mechanistic detail, predictive power, and fidelity to reality of current fugacity-aquivalence fate models for metals by estimating aqueous speciation and complexation, rather than relying on empirically derived partition coefficients. A pseudo-steady state version of TRANSPEC was used to simulate Zn dynamics in Ross Lake (Flin Flon, MB, Canada) that received elevated metal and organic matter inputs for over 50 years. Field studies revealed that ZnS forms soluble ZnL, Zn2+, and ZnSO4(0) increasing pore water concentrations when surficial sediments turn oxic during fall. The model results for three seasonal scenarios suggest that Zn remobilization is driven by resuspension of insoluble ZnS and the contribution of diffusion is negligible, even during fall when ZnS dissolves to increase the concentration of soluble species under oxic conditions in the sediments. The low diffusive flux is due to the binding of Zn to colloidal dissolved organic matter (DOM) for which sediment-water diffusion is relatively slow, a result that was obtained as a result of considering metal speciation in the fate calculations.
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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 | 0.000 | 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".