Solid-Solution Partitioning of Metals in Contaminated Soils: Dependence on pH, Total Metal Burden, and Organic Matter
Bibliographic record
Abstract
Environmental risk assessment of metals depends to a great extent on modeling the fate and the mobility of metals based on soil−liquid partitioning coefficients. A large variability is observed among the reported values that could be used to predict metal mobility and bioavailability. To evaluate this, soil−liquid partitioning coefficients ( K d ) for many elements but especially for the metals cadmium, copper, lead, nickel, and zinc were compiled from over 70 studies of various origins collected from the literature. The relationships between the reported values are explored relative to variations in soil solution pH, soil organic matter (SOM), and concentrations of total soil metal. The results of multiple linear regressions show that K d values are best predicted using empirical linear regressions with pH (with R 2 values of 0.29−0.58) or with pH and either the log of SOM or the log of total metal and with resulting R 2 values of 0.42−0.76. A semi-mechanistic model based on the competitive adsorption of metal and H + [dependent on solution pH, total metal content, and log(SOM)] was a better tool to predict dissolved metal concentrations (with R 2 values of 0.61−0.88), with the exception of Pb (at 0.35).
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".