Application of filtered tailings storage method at Tüprag Efemçukuru Gold Mine
Bibliographic record
Abstract
The filtered mine waste management method is a good alternative for sustainable mining and minimising environmental footprint. This method was applied for the first time at the commercial level in Turkey in 2011 at Tüprag Efemçukuru Gold Mine. At the Tüprag’s Efemçukuru Gold Mine (Efemçukuru), half of the dewatered filter cake is pumped to underground as paste backfill, the other half (filtered tailings) is stored in a surface tailings storage facility (TSF). The tailings material designated for surface is dewatered to approximately 80% solids through the use of thickening and filter presses and turned into a filter cake. The filtered tailings stockpile is a lined facility constructed with a double geosynthetic low permeability base liner system and includes a leakage detection system. Mining Association of Canada’s (MAC) Towards Sustainable Mining (TSM) principles and Global Industry Standard on Tailings Management (GISTM) are being applied for the management of the storage facility. Filtered tailings storage methods have advantages over other tailings deposition methods including increased physical stability of the stored heap due to the decrease in the waste saturation, and mitigation of potential failure mechanisms (e.g. liquefaction) as long as the tailings material is sufficiently compacted. Filtered tailings are also placed at an increased density, which reduces the facility footprint. Furthermore, deposition at a low water content means an active pond is not required within the TSF which allows for progressive reclamation. In this article, practical experience gained through the design, testing, construction and operation of the Efemçukuru filtered TSF is presented as well as the discussion of the ongoing implementation of Best Available Practices (BAP) and Best Available Technologies (BAT) in the site’s mine tailings management practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".