A new approach to constrained open pit pushback design using dynamic cut-off grades
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".