Comparing WHAM 6 and MINEQL+ 4.5 for the Chemical Speciation of Cu<sup>2+</sup> in the Rhizosphere of Forest Soils
Bibliographic record
Abstract
Metal speciation data calculated by modeling could give useful information regarding the fate of metals in the rhizospheric environment. However, no comparative study has evaluated the relative accuracy of speciation models in this microenvironment. Consequently, the present study evaluates the reliability of free Cu ion (Cu2+) activity modeled by WHAM 6 and MINEQL+ 4.5 for 18 bulk and 18 rhizospheric soil samples collected in two Canadian forested areas located near industrial facilities. The modeling of Cu speciation was performed on water extracts using pH, dissolved organic carbon (DOC), major ions, and total dissolved Al, Ca, Cu, Mg, and Zn concentrations as input data. Four scenarios representing the composition of dissolved organic substances using fulvic, humic, and acetic acids were derived from the literature and used in the modeling exercise. Different scenarios were used to contrast soil components (rhizosphere vs bulk) and soil pH levels (acidic vs neutral to alkaline). Reference Cu2+ activity values measured by an ion-selective electrode varied between 0.39 and 41 nM. The model MINEQL+ 4.5 provided good predictions of Cu2+ activities [root-mean-square residual (RMSR)= 0.37], while predictions from WHAM 6 were poor (RMSR = 1.74) because they overestimated Cu complexation with DOC. Modeling with WHAM 6 could be improved by adjusting the proportion of inert DOC and the composition of DOC (RMSR = 0.94), but it remained weaker than predictions with MINEQL+ 4.5. These results suggested that the discrepancies between speciation models were attributed to differences in the binding capacity of humic substances with Cu, where WHAM 6 appeared to be too aggressive. Therefore, we concluded that chemical interactions occurring between Cu and DOC were key factors for an accurate simulation of Cu speciation, especially in rhizospheric forest soils, where high variation of the DOC concentration and composition are observed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".