The evolution of co-disposal stopes at Newmont Tanami Operations
Bibliographic record
Abstract
As underground operations mine at greater depths, the haulage of waste rock to the surface becomes a constraint on efficient operation. By keeping waste rock underground, more trucks are available to haul ore to the surface. Additionally, the increasing cost of transporting cement to remote mine sites, and paste production costs in general require continuous optimisation. This is the case at Newmont’s Tanami Operations, where there is a focus on keeping waste underground, and optimising cement usage. An outcome of this is the development of co-disposal, that is, depositing waste rock in what would traditionally be a fully paste filled stope, and encapsulating the waste rock in paste to allow safe mining of adjacent stopes. To achieve this, consideration is given to stope geometry, a suitable rock tipping location, stope exposures and filling rates, all assessed against total expenditure and mine scheduling. Modelling of the fill scenario is completed in Deswik, and Flac3D is used to confirm paste strength requirements to achieve the required Factor of Safety, and for optimised binder contents. By depositing waste rock in paste filled stopes, costsavings are generated by displacing paste, and by reducing waste haulage costs. The development and fine-tuning of the co-disposal fill methodology to date has kept 325,000 tonne of waste rock underground, generating cost-savings exceeding $2.5 million, compared to fully paste filled stopes. This paper presents the evolution and optimisation of co-disposal stopes at Tanami since 2020, including the challenges experienced, the cost-savings, and value added to the operation.
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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".