Design of the Eldorado Gold Efemçukuru filtered tailings facility
Bibliographic record
Abstract
Eldorado’s Efemçukuru Project incorporates the design of a filtered tailings facility to store 1 million cubic metres of dry stack filtered tailings in a high seismicity region of Western Turkey. Compared to conventional tailings technologies, dry stack filtered tailings have many advantages in terms of lowering long-term environmental liability/impacts (for example through decreased potential seepage, minimized storage footprints, and ease of progressive reclamation). An important design feature of this project is the incorporation of a fullylined base (HDPE/GCL double-liner) coupled with an underdrain system for seepage control at the base of the facility, as the tailings are potentially acid-generating. Increased structural integrity is achieved through compaction of tailings filter cake material in lifts, the construction of rock toe berms on the downstream side of the pile, and selection of a double-liner system that will provide sufficient frictional strength in this high seismic zone. The use of a filtered tailings and liner system in the Eldorado Gold Efemçukuru project provides added protection of groundwater resources and represents another step in tailings waste management and environmental stewardship in the industry.[All papers were considered for technical and language appropriateness by the organizing committee.]
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".