Barrick’s Turquoise Ridge Gold Mine Optimizes Underground Production Scheduling Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mining operations determine a long-term production schedule, often to maximize net present value. For a time horizon of between years and decades, optimization models seek the extraction times—with monthly or yearly fidelity—of three-dimensional, notional blocks of ore and waste within a deposit to satisfy spatial precedence constraints, as well as resource constraints on the amount of material extracted and sent to the mill. With algorithmic advances, as well as those in mine planning software and in hardware, we are able to solve instances with a decade-long horizon at daily fidelity. The resulting objective, repeatable, and defensible schedules inform production and maintenance supervisory decisions based on resource availability, that is, loaders, shovels, haul trucks, and mineral processors. We implement our solutions at the Turquoise Ridge underground gold mine in Nevada, United States. These solutions indicate more than a 2% increase in total ounces extracted over a decade while decreasing development footage by as much as 11% over the same time horizon. Furthermore, we are able to incorporate rules governing a shared resource and to evaluate binding versus nonbinding capacity constraints.
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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 | 0.001 |
| 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 it