Probabilistic-Based Stope Design Methodology for Complex Ore Body with Rock Mass Property Variability
Bibliographic record
Abstract
This paper presents a probabilistic approach for optimizing stope design methodology while taking into consideration the variability in the rock mass properties. For this study, a complex orebody in a Canadian mine was used. Because of the variability in the rock mass properties of the orebody, it was not possible to determine precisely, the values of geotechnical design input parameters and hence the need to utilize a probabilistic approach. Point Estimate Method (PEM), a probabilistic tool, was incorporated into numerical analysis using FLAC 3D to study the deformation magnitudes of various stope geometries to determine the optimal stope geometry with a minimum ground control problem. Results obtained for the distribution of the wall deformations and the floor heaves for each option of the stope geometry were compared to select the best geometry to achieve the optimum stability condition. The methodology presented in this study can be helpful in the process of underground mine planning and optimization in complex orebody.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".