Open pit optimisation using discounted economic block values
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Bibliographic record
Abstract
Strategic mine planning and the management of the future cash flows are a vital core of surface mining operations. The time dimension, which is an integral part of the scheduling problem, is not embedded in traditional ultimate pit outline optimisation algorithms. This study explores the validity of the theorem that a pit outline determined by an optimal long term schedule algorithm is constrained by the conventional Lerchs and Grossmann's (LG) optimised pit outline. This hypothesis was investigated through a case study using the intelligent open pit simulator (IOPS) founded on agent based learning theories. The optimal pushback schedule was determined using IOPS before determination of the optimised final pit outline. The economic block values were discounted with respect to the allocated extraction time, followed by final pit limits optimisation using LG algorithm.
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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 it