Approach to tailings facility liner configuration selection: A case study
Bibliographic record
Abstract
What liner configuration should you use for a tailings facility? Do you need a geosynthetic liner? Should you include overdrains above your liner? This paper presents an approach to answer these questions for a case study tailings facility in a semi-arid environment. The case study project includes a performance criterion that the seepage flux be limited below the impoundment. Four liner configurations were shortlisted, and the efficacy of each arrangement compared with numerical modelling. The configurations are: (1) tailings directly over a liner, (2) tailings on an overdrain system over a liner, (3) tailings on an overdrain system over a blinding layer over a liner, and (4) sealing the foundation with tailings slimes (i.e., no geosynthetic membrane). This paper also presents estimates of the longevity of an HDPE liner system, as this is vital in assessing performance over very long time periods. Seepage and consolidation modelling results indicate that overdrains do not improve the potential to meet the performance criterion and overdrains have a limited post-closure consolidation benefit. All configurations with geosynthetics indicated the liner system would function adequately during the period prior to geosynthetic membrane degradation. The slimes sealing case was shown to not provide an adequate impedance to flow.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".