Bibliographic record
Abstract
The identification of high-value, practical mine plans is a complex and challenging process. Mineral prices are a crucial factor to the value of a project and, due to the inherent volatility of the commodities market, also the most uncertain. Despite this reality, conventional planning processes don’t sufficiently account for the financial uncertainty associated with mining assets. As a consequence, life-of-mine schedules must often be extensively revisited as the fiscal climate changes. The inherent financial uncertainty of mining projects presents a substantial risk to stakeholders and without proper mitigation can easily reduce the perceived and real value of a mining asset. The risks associated with uncertainty can be mitigated through the creation of robust mine schedules. In this paper, two distinct approaches to creating robust, optimised long-term underground mine schedules will be presented. The Genetic Optimizer for Stochastic Problems (GOSP) method incorporates price distributions, rather than fixed projections, into the optimisation process. The Horizon method incorporates the concept of ’management flexibility’ into the process of schedule optimisation. As will be demonstrated by a case study, these methods, when integrated with the Schedule Optimization Tool (SOT), produce robust, high net present value schedules for underground mining operations.
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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".