A new approach to constrained open pit pushback design using dynamic cut-off grades
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Bibliographic record
Abstract
An integral part of open pit optimization is deciding which section of the ultimate pit to mine during a specific period. For a given period there are often operational and marketing constraints that restrict what can be removed or processed. The operational constraints arise from a number of different limitations such as safe slope of internal mining walls, mill and mining capacity. Traditional methods for pushback (phase) design that incorporate these constraints are ad-hoc and can lead to suboptimal solutions. Another important optimization decision that must be made is the cut-off grade to be used for a specific period. In this paper, a new method is presented that generates near maximal expected profit and dynamically defines the optimal cut-off grade for each mining period or pushback over the life-of-mine, thus deciding whether a block is ore or waste during the optimization process. More specifically, a method for converting a fractional linear program solution into an integral solution known as pipage rounding is applied to an integer program formulation of a pushback design optimization problem. The proposed method aims to produce a set of pushbacks in a way that the total discounted profit to be generated through production scheduling is maximized. Two case studies demonstrate the applied aspects of the proposed method.
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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 | 0.002 | 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 | 0.001 |
| 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 it