Integrating a new approach at the Westwood mine site for predicting the stope mined geometry
Bibliographic record
Abstract
Open stoping has become a popular mining method in hard rock mines, not only due to the safety of the method as a non-entry approach, but also because of the high extraction rate and low costs. At mine sites, stope performance is evaluated by calculating stope overbreak using the Stability Chart. The limitations of the Stability Chart regarding the precision of the predictions, non-consideration of factors such as the influence of blasting, and the exclusion of underbreak have led to suboptimal designs. The modern capabilities of computers have resulted in large amounts of data being collected and despite subsequent statistical models being more capable, they have been underutilised in the stope design process. To increase the information and knowledge that is extracted from the data and to progress from the simple qualitative per stope face prediction that is provided by a traditional Stability Chart approach, the Australian Centre for Geomechanics has developed a design approach that can account for many of the variables that influence stope performance and uses multivariate modelling methods to forecast the expected stope geometry. This approach is implemented as a stope reconciliation and design application and is integrated in mXrap software that allow users to import their stope design as well as their blasting design and predict the expected mined geometry for stope planification and optimisation. This paper presents a case study of how the stope reconciliation and design application has been integrated at Westwood mine to understand and predict stope performance. An overview of the approach, the analysis of past stope performance and the generation of future predictions is presented along with the utility of this approach for optimising stope performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".