The Qualitative Stability Graph for Open Stope Design – Recent Developments
Bibliographic record
Abstract
ABSTRACT: The Stability Graph is an accepted tool for open stope design in the metalliferous underground mining community. Since its development in 1981, it has undergone several modifications and remains active research subject to date. Admittedly, while some of the suggested changes have been called into question for their practical relevance, others have gone a long way to improve the reliability of the method in minimizing dilution in open stope mining. One of the major concerns that has emerged in researching some suggested modifications to the design tool appears to show some authors do not understand the assumptions and purpose behind the tool. Such authors rely on application of statistics to the database with no idea of the practical implications of the outcome of their analysis. The downside of this is that so much confusion has been created in the mining industry as to what is useful in using the method for open stope design. This paper cautions against the misuse of statistics in geo-engineering with emphasis on the Stability Graph and provides recent developments relevant to improving the reliability of the method in reducing dilution in open stope s. Furthermore, the long outstanding question of whether the original Stability Graph number factors or the modified Stability Graph number factors should be used or not is that there is no difference between the two. 1. INTRODUCTION In 1980 the Department of Energy, Mines and Resources of the Canadian Center for Mineral and Energy Technology (CANMET) commissioned Golder Associates Consulting Geotechnical and Mining Engineers to determine the information that is required to predict stable spans for open stopes at mining depths below 1 000 m. The method developed in the report for the design of open stopes by Golder Associates has since been referred to as the Mathews Stability Graph Method (Mathews et al., 1981).
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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 | 0.002 | 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".