Performance evaluation of a new stochastic network flow approach to optimal open pit mine design-application at a gold mine
Bibliographic record
Abstract
The optimal design of production phases and ultimate pit limit foran open pit mining operation may be generated using conventionalor stochastic approaches. Unlike the conventional approach, thestochastic framework accounts for expected variability anduncertainty in metal content by considering a set of equallyprobable realizations (models) of the orebody. This paper evaluatesthe performance of a new stochastic network flow approach for thedevelopment of optimal phase design and ultimate pit limit using agold deposit as the case study. The stochastic and conventionalframeworks as considered here utilize the maximum flow andLerchs-Grossman (LG) algorithms, respectively. The LG algorithm isrestricted to considering an estimated (average-type) orebodymodel, while the stochastic maximum flow algorithm is developed tosimultaneously use a set of simulated orebody realizations as aninput. The case study demonstrates that, when compared to theconventional LG algorithm as used in the industry, the stochasticapproach generates a 30 per cent increase in discounted cash flow, a21 per cent larger ultimate pit limit, and about 7 per cent moremetal, while it maintains a consistency in phase size.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".