Effects of estimates from different geochemical models on metal fate predicted by coupled speciation-fate models
Bibliographic record
Abstract
Coupled metal speciation-fate models are an improvement over stand-alone fate-transport models for accurately assessing metal fate and transport. These coupled models estimate fate-controlling partition coefficients using geochemical speciation/complexation models. Commercially available geochemical models are practical options for a two-step, loose coupling with fate-transport models. These models differ in their partitioning estimates because of differences in assumptions, databases, and so on. The present study examines the effects of differences in estimates from geochemical models on estimates of cationic metal fate using two geochemical models: the Windermere humic aqueous model (WHAM) and the minicomputer equilibrium+ model (MINEQL+). The results from each geochemical model were used as input to the fate module of TRANSPEC (a general, coupled metal transport and speciation model). The two versions of the TRANSPEC model were then used to assess the fate of five cationic metals (Cd, Cu, Ni, Pb, and Zn) in Ross Lake (Flin Flon, MB, Canada; alkaline, eutrophic, mine impacted), Kelly Lake (Sudbury, ON, Canada; circumneutral, mesotrophic, mine influenced), and Lake Tantaré (Quebec City, QC, Canada; acidic, oligotrophic, pristine). For relatively soluble metals (Cd, Ni, and Zn), the WHAM and MINEQL+ estimates of speciation/complexation were similar for Ross and Kelly lakes but differed for Lake Tantaré. These differences, however, did not result in significant differences in overall fate estimates. Marked differences were observed between the WHAM and MINEQL+ estimates of partition coefficient, Kd, for more particle-reactive Cu and Pb that translated into the greatest impact on fate in mesotrophic Kelly Lake, in which particle movement is important for fate.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".