Discrete rate simulation for geostatistically informed economical evaluation of narrow vein Au-Ag ore processing
Bibliographic record
Abstract
Increasing demand for various metals, including gold and silver, has initiated a favorable cycle for mining investors. However, mining projects remain risky due to the significant investments required and project-specific technical factors that are subject to geological uncertainty. Specifically, narrow vein mining suffers from a lack of geometrical freedom in the advance of the excavation; therefore, mine planners must compensate by controlling stockpile and blending and metallurgical process variables. Nonetheless, this lack of geometrical freedom makes it possible to link geological uncertainty to dynamic functioning of the process and ultimately to the net present value and internal rate of return. Discrete rate simulation is an effective approach to dynamic mass balancing, in which geometallurgical relationships can be implemented considering geostatistically variable incoming combinations of andesitic and rhyolitic ore in the case of narrow vein Au-Ag mining. The limited geometrical freedom is conducive to a simple mining sequence, easily implemented within a discrete rate simulation that includes a stochastic representation of the narrow vein orebody based on sequential indicator simulation. The resulting tool uses the apparently disadvantageous geometry of narrow veins and is effective at capturing project-specific metallurgical variables within economic prefeasibility and feasibility studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".