A flexible mine production model based on stochastic price simulations: application at Raglan mine, Canada
Bibliographic record
Abstract
The conventional economic evaluation technique that is currently used to evaluate the economic viability of mining operations has three important pitfalls. First, the probability distributions of key variables fed into the simulation process are in most part subjective and not based in a solid scientific ground. Second, the simulation method applied to generate metal prices paths results in unrealistic jumps and falls between the consecutive discrete time steps throughout the same simulated path. Third, the conventional technique implements a static production model in which the flexibility to alter the production policy is not applicable. These three pitfalls can impact the accuracy of evaluation results and consequently can lead to suboptimal production decisions. This paper presents an economic evaluation technique for mining projects based on the real options theory. This technique is based on generating future simulated metal price paths using the appropriate stochastic process for each metal. More important, the technique implements a flexible production model in which the production policy can be revised according to the new market information. To illustrate the difference the proposed improvements can make in the evaluations results, both the conventional and the new technique are applied to investigate the economic viability of some marginal mining zones based on data from Xstrata's Raglan mine. It has been found that the differences in the evaluation results between the two techniques range between $CAD0˙82 million and as high as $CAD3˙25 million depending on the size of the subzone, and for the total zone value, the difference is $CAD6˙55 million.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".