Multi-lifts selection using scenario simulation and financial metrics
Bibliographic record
Abstract
In evaluating a block caving project, one of the most important strategic decisions is the location of the extraction level. This evaluation is complex for a single lift; therefore, finding the optimum combination of multiple lifts is much more challenging and, in most cases, sub-optimal since the decision is largely influenced by the selection of one of them and not the overall value. This paper proposes the option to simulate as many scenarios as possible, analysing the size of each lift based on multiple evaluations using variable shut off grades and production rates. This analysis enables exploring many strategies and using the hill of value technique to identify areas of optimum solutions in a reasonable time. For example, starting with a small Lift1 extracting high grade with a low production target to reduce initial capital cost and ramp-up to maximum production capacity in the second lift versus the option to achieve maximum production rate since the beginning of Lift1. In addition, the decision will consider financial investment metrics like net present value and internal rate of return. This methodology is demonstrated with a real case to show the potential impact on the value of the project.
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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".