Optimising combined open pit and underground strategic plan
Bibliographic record
Abstract
This paper examines the case of a large open pit that is being planned at the site of an existing underground mine. Strategic planning work is undertaken to investigate the project value associated with an expansion of underground operations in conjunction with the proposed pit. Previous analysis by mine planners has determined that any depletion of the available open pit resource that changes the optimum pushback sequence will have a significant negative impact on overall project value. As a result, the main objective of this case study is to determine a strategic plan capable of yielding the optimum economic value for a combined underground–open pit operation.To investigate the viability of an underground expansion, a system to identify the stope resource that would add value to a combined operation if mined ahead of the pit is required. The main solution to this problem is the use of a resource model variable that defines the blocks which have greater discounted value if mined by stoping rather than by the open pit. From this potential underground resource, a series of mineable stope shells are generated at various cut-offs. This stope reserve data formed the input to an optimisation process used to optimise the underground mining plan, at a conceptual level, for various project configurations. Then, a dynamic programming mathematical program is used to evaluate the optimum value of a combined open pit and underground operation. The results generated in the case study presented herein provide a clear focus and direction for the next level of detailed mine design and planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".