Preliminary geotechnical assessment of the potential use of mixtures of soil and acid mine drainage neutralization sludge as materials for the moisture retention layer of covers with capillary barrier effects
Bibliographic record
Abstract
Lime treatment of acid mine drainage (AMD) generates sludge that is commonly stored in ponds for dewatering. The use of soil-aged sludge-based mixtures for mine site rehabilitation can allow the emptying of existing basins, thus extending their storage capacity, reducing the volume of the borrow soil pit required for mine site rehabilitation, and consequently reducing the mine footprint. The authors investigated the geotechnical properties of silty soil–sludge mixtures (SSMs) as possible components of covers with capillary barrier effects (CCBEs) to prevent AMD generation from mine waste. SSMs with β values of 10%, 15%, 20%, and 25% sludge (β = wet sludge mass / wet soil mass) were studied. Two water contents were considered for each of the mixture components: 175% and 200% for the sludge and 7.5% and 12.5% for the soil. Results indicate that saturated hydraulic conductivity (ksat) values were in the range of 10−5 cm/s for the soil and SSMs at void ratios ranging from 0.28 to 0.53, with values decreasing slightly when β was increased from 0% to 25%. The air-entry value (AEV) increased from 20 kPa for the soil alone to 35 kPa for the SSM with β = 25%. These values of ksat and AEV are comparable to those of materials used in the moisture retention layers of existing efficient CCBEs. However, the volumetric shrinkage increased from about 2% for the soil alone to values ranging between 24% and 32% for the SSM with β = 25%, depending on the initial water contents of the components. Tools are provided to estimate to which extent the use of sludge in SSMs can reduce the volume of borrow natural soil required for a moisture retention layer of a CCBE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".