Diffusion of metals in geosynthetic clay liners
Bibliographic record
Abstract
The potential for using geosynthetic clay liners (GCLs) to mitigate metal mobility is dependent, in part, on consideration of its effectiveness as a diffusive barrier. In this context, laboratory-measured diffusion coefficients and supporting sorption data for metals (Al, As, Cd, Cu, Fe, K, Mg, Mn, Ni, Sr, Zn) are reported for the following four cases where a GCL might serve as an effective barrier material to metals and metalloids: acidic rock drainage, gold mine tailings, lime-treated mine effluent, and municipal solid waste. The average diffusion coefficients for Cu, Cd, Zn, Fe and Ni covered a narrow range from 0.67 × 10−10 to 0.89 × 10−10 m2/s. The diffusion coefficients for As, Al, Mg, Mn and Sr were in the range D = 0.80 × 10−10 to D = 1.6 × 10−10. The diffusive movement of other more common inorganic ions such as Ca, Na, S (SO4) and Cl are also considered, as well as the impact of pH, Eh and speciation on metal mobility in GCLs.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".