Heuristic stope layout optimisation accounting for variable stope dimensions and dilution management
Bibliographic record
Abstract
Underground mining operations are managed in an environment that is surrounded by a series of complex geotechnical, operational and economic restrictions; and operation costs increase mainly because underground mining goes deeper. As a response to this increase, mine planning has potential to improve operation performance, profitability and productivity. Stope layout is a decision-making problem based on determining stopes to be produced under economic, geotechnical and operational constraints. In this paper, a further step is taken and optimisation approach considers minimisation of inherent internal dilution in addition to conventional profit maximisation. This proposed approach can also deal with multiple sectors and variable stope dimensions. The application in gold deposit showed that a profitable stope layout with minimum internal dilution could be generated while respecting geotechnical and operational requirements. In addition, the formulation presented herein can be extended to evaluate multi-metal cases, the mine production scheduling and incorporate ore body uncertainty into optimisation.
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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 | 0.000 |
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".