Resource Extraction Evaluation Using a Mathematical Programming Framework for Surface-Underground Mining Options and Transitions Optimization
Bibliographic record
Abstract
A mathematical programming framework based on Mixed Integer Linear Programming (MILP) model for surfaceunderground mining options and transition optimization for resource extraction is presented in this paper. Existing models are mainly based on a stepwise optimization approach with limited constraints which produces localized optimal solutions and are often impractical. For mineral deposits amenable to both surface and underground mining options, the MILP framework determines the most suitable mining option and associated schedule to exploit the orebody. The MILP formulation is tested and implemented on a gold deposit case study. The NPV of the optimal mining option ($ 2.515 billion) is sensitive to the gold price, ore quantity delivered from the underground mine, and delay factor associated in supporting the operational development and stopes. Positive changes in the delay factors associated with operational development support and mining stope support have more impact on the NPV than negative changes. However, the NPV is highly sensitive to the mining stope support delay than the operational development support delay
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".