Dynamic simulation of multi-phase mining venture risks resolution
Bibliographic record
Abstract
Dynamic resolution of mining venture risks and their effects in venture valuation significantly affect a venture’s profitability and viability. Valuation methods must account for this dynamic behaviour of venture risks to provide appropriate weighting factors for long-term cash flows. Conventional methods use single risk-adjusted rates to account for venture risk, resulting in severe underestimation of long-term venture cash flows. In this study, the authors develop the dynamic risk model (DRM) 1 for valuing long-term multi-phase mining ventures. This model uses the capital market variance sensitivity ratio (VSR) to derive the expected venture return from the venture and market risk structures. The DRM is used to assess the Moose Gold Venture on the Toronto Stock Exchange (TSE 300). The dynamic stochastic model of the venture’s value is simulated over its varying phase risk structure using the Monte Carlo technique. Analysis of the results from the DRM and the discounted cash flow (DCF) methods shows that, for long-term multi-phase mining ventures with varying underlying uncertainties, the use of single rates significantly penalizes long-term cashflows. The value of the 20-year venture using DCF is about 50 percent of that using DRM. The DCF method also underestimates the venture’s value by about 10, 30 and 60 percent in phases III, IV and V, respectively. Pronounced effects of this problem and venture delays result in the loss of substantial value which may lead to the rejection of profitable ventures. With over a 3-year delay, the venture’s value is zero using DCF and $43 million using DRM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".