Data-Driven Dynamic Simulations of Gold Extraction Which Incorporate Head Grade Distribution Statistics
Bibliographic record
Abstract
The Alhué mining district, Chile, is an example of a high-grade Au-Ag-Zn(-Pb) deposit with mineralized veins that contain variable amounts of copper sulfides, which are detrimental to the cyanidation process. Similar deposits can be found in the central zone of Chile, with polymetallic veins (Au, Ag, Cu, Pb, and Zn) that are related to subvolcanic intrusive events, the development of collapse calderas, and extensive hydrothermal alteration, such as Bronces de Petorca, the Chancón mining district and Cerro Cantillana; areas of the world with similar formations include the western United States and the Henan Province in central China, for example. Mineralogical variation can be managed within the metallurgical process by alternating its operational modes. The decision to switch between modes is governed by current and forecasted stockpile levels feeding into the process, according to a discrete rate simulation (DRS) framework that has now been developed to incorporate head grade data for gold. Customized simulations that incorporate probability distribution models using head grade have now been developed, following a statistical analysis based on data from the Alhué district. This study applies data-driven simulation modeling to represent standardized operational modes and their impact on the operational performance of gold extraction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".