Effects of High-Order Simulations on the Simultaneous Stochastic Optimization of Mining Complexes
Bibliographic record
Abstract
A mining complex is composed of mines, mineral processing streams, stockpiles, and waste facilities, which culminate with generated products that are delivered to customers. The supply uncertainty and variability of materials extracted from the mines, which flow through a mining complex to generate products, can be quantified through geostatistical simulations and can be used as inputs to the simultaneous optimization of mining complexes. A critical aspect to consider is that mineral deposits are characterized by spatially complex, non-Gaussian geological properties and multiple-point connectivity of high-grades, features that are not captured by conventional second-order simulation methods. This paper investigates the benefits of simultaneously optimizing a mining complex where the simulations of the mineral deposit are generated by a high-order, direct-block simulation approach. The optimized life-of-mine (LOM) production schedule is compared to a case in which the same setting is optimized by having the related simulations generated using a second-order simulation method. The comparison shows that the incorporation of simulations that reproduce the spatial connectivity of high-grades results in a more informed LOM production schedule. The sequence of extraction is driven by the spatial connectivity of high-grades, resulting in a mill throughput with better material quality and reduced waste extraction. Furthermore, the discounted cash-flow increases by more than 5% as compared to the case in which the second-order simulations are used.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".