Bibliographic record
Abstract
Mitigation of acid mine drainage (AMD) generated from the surface disposal of mine tailings (MT) containing sulphide minerals is one of the major challenges facing the mining industry today. AMD results in acidic effluents containing elevated concentrations of sulphates and metallic and metalloid elements. It can thus cause severe contamination of soils, surface and groundwater, along with devastating environmental damage. Paste backfill is a relatively new technology whereby MT are solidified/stabilised using binders and processed back into the mined space. The cogent reasons supporting this technique include lower operating costs, efficient ground support, reduction in the amount of waste to be disposed of on the surface and significant environmental benefits through better control of pollutants. An additional advantage is that industrial by-products such as cement kiln dust (CKD) and fly ash can be used alone or in combination with Portland cement to enhance the properties of paste backfill and reduce binder cost. The goal of the present research is to examine the strength development of paste backfill prepared using sulphidic MT and industrial by-products (e.g. CKD and fly ash) with or without Portland cement. Furthermore, the pH and sulphate content of paste backfill mixtures was monitored to provide information on both the tailings oxidation process and potential sulphate attack in paste backfill. The findings indicate that utilising industrial by-products in the preparation of paste backfill reduced the availability of calcium aluminate compounds susceptible to sulphate attack associated with cemented paste backfill containing ordinary Portland cement. It is also shown that developing an efficient technique for AMD control through making paste backfill from sulphidic MT can be both feasible and cost-effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".