Application of risk-based tailings management at Tüprag Efemçukuru Gold Mine
Bibliographic record
Abstract
Eldorado Gold Corporation, and its subsidiary Tüprag Metal Madencilik San.Tic.A.S. (Tüprag), owns and operates the Efemçukuru Gold Mine (Efemçukuru) in Turkey. As part of their commitments to their Mining Association of Canada (MAC) membership, Efemçukuru joined MAC’s Toward Sustainable Mining (MAC-TSM) initiative in 2015, followed by an evaluation of the site’s tailings management system. The findings showed that the tailings management system at Efemçukuru had several well-established operational controls that were regularly and properly applied; however, opportunities for improvement and adoption of best available practices to the current tailings management system were identified (Norwest Corporation 2018). A program to meet or exceed all waste management protocols was started in 2016 with the mine achieving MAC AAA designation in 2021. The tailings management system proposed by MAC follows a risk-based approach, meaning that the risk assessment process should be incorporated into the routine decision-making process, performance evaluation, and supported with documentation and records. In risk-based management/approach, site-specific risk assessment, potential high-consequence events, critical controls, trigger-action-response-plan, emergency response plan, emergency preparedness plan, performance assessment and management assessment improvement have an important place. Consistent with systems such as the environmental management system ISO14001, the tailings management framework follows the plan-do-check-take action cycle and ensures that a management model is established for control and continuous improvement. In this article, Tüprag presents a case history of its practices in the management of the Efemçukuru tailings and mine rock storage facilities in accordance with MAC-TSM and its current efforts to align them to the Global Industry Standard on Tailings Management (GISTM). Although the results appear straightforward, significant effort and insight was required to update and implement the tailings management plan. This article is intended as a reference for mines that have not yet fully implemented MAC-TSM and GISTM standards and as an informative case history for mines that have already done so.
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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".