Analysis of dry cover systems composed of tropical soils for mining waste
Bibliographic record
Abstract
The flow of water in mining tailings and waste rock may cause serious environmental impacts associated with acid rock drainage. Soil cover systems have been used to control or minimise such damages. In relatively dry climates, ‘store-and-release’ covers, also known as dry covers, have been considered as feasible alternatives. The objective of this paper is to evaluate the influence of atmospheric conditions and soil type in the performance of dry cover systems, considering conditions typical of tropical climates found in Brazil’s centralwest region. Numerical analyses using the finite element method were performed considering four different systems composed of tropical soils. Among the proposed arrangements, three use a soil that presents a bimodal soil–water characteristic curve and the fourth cover employs a unimodal soil. The thicknesses of intermediate materials were varied, and different representative precipitation parameters were considered for a period of one year. The results obtained were compared in terms of the internal flow and the capacity of the system to store water. The results indicate that bimodal soils may not be ideal cover materials and require specific compaction conditions that would reduce their macropores. Unimodal tropical soils presented adequate response as store-and-release materials. The use of an intermediate layer acting as a capillary barrier did not offer significant improvement to the cover systems evaluated.
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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".