Metal retention in geosynthetic clay liners following permeation by different mining solutions
Bibliographic record
Abstract
The leaching of hazardous metals and metalloids from mine tailings is a significant problem facing the mining industry. Although, in the past, geosynthetic clay liners (GCLs) have primarily been employed as leachate barriers in landfills, recent times have seen an increase in the variety of their applications, including applications in the mining industry. The capacity of GCLs to attenuate metals and metalloids (As, Al, Cd, Cu, Fe, Mn, Ni, Sr, Zn) from mine acidic rock drainage (ARD) water and a neutral-pH, As-rich water associated with gold mine tailings (GMT) was evaluated. Water-prehydrated GCLs were permeated with GMT and ARD for short (2 and 5 pore volumes, PV) and long (21 PV) periods. The long-term hydraulic conductivity of the GCLs increased from 1.6 × 10-11 m/s (water for 5 PV) to 5.0 × 10-11 m/s and 1.3 × 10-10 m/s after permeation with the GMT and ARD waters, respectively (21 PV). The distribution of metals within the GCL was quantified in order to differentiate between metals associated with precipitated compounds, soluble complexes in porewater, and sorbed metals. Metals sorbed to the GCL are reported in micrograms of metal per gram of bentonite (ppm), and are indicative of the GCL's sorption capacity for a barrier system. Significant differences existed between the soil tested at 2 PV 5 PV and 21 PV It was only at 5 PV that the precipitation of the ferrihydrite occurred in the ARD samples, and gypsum occurred in the GMT samples. These minerals were responsible for retention of metals in addition to the cation exchange of the GCL.
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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".