A stochastic mine planning approach to determine the optimal open pit to underground mining transition depth – case study at the Geita gold mine, Tanzania
Bibliographic record
Abstract
Several of the world's largest open-pit mines are expected to consider making a transition to underground mining because of the opportunity to access an increased amount of reserves and extend a mine's life. A case study exploring the optimal transition depth from open pit to underground mining at AngloGold Ashanti's Geita gold mine in Tanzania is presented herein. The approach considered assesses the problem by evaluating the profits of a set of candidate transition depths, which have been identified by the mining operation as viable opportunities. An accurate valuation for each candidate's transition depth is derived by producing yearly mine plans based on uncertainty, which outline expected yearly cash flows. Compared with the conventional deterministic approach, the results of this study show a 23% net present value increase for the stochastic mine plans, as well as an improved production performance and the ability to meet mill requirements throughout the life-of-mine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".