An Investigation into the Stand-up Time of Stopes at the Birchtree Mine, Thompson, Manitoba
Bibliographic record
Abstract
Open stoping is a common mining method employed in the Canadian mining industry.Extracting large volumes of rock can present stability issues which can affect productivity and safety.Many factors such as changing stress states, rock mass structure, and intact rock strength can contribute to instability.One factor not commonly assessed when examining stope stability is exposure time.With increased exposure time, the rock quality of the opening tends to degrade.Birchtree Mine located in Thompson, Manitoba, is the focus of this study due to the time dependent instability that has been observed.For the proposed project, an empirical method was chosen since these methods can easily be updated with future case histories to better reflect onsite conditions.Few empirical methods exist for assessing exposure time for open stope mining.The most common method of incorporating time with stability is Bieniawski's 1989 RMR Stand-up Time Graph.As part of this project the original data used to create the RMR Stand-up Time Graph was reinterpreted so it could be plotted on the Stability Graph, which is used for open stope design.Case histories from the Birchtree mine, along with the original database for Bieniawski's empirical method, have been examined.Comparisons were conducted to evaluate the accuracy and validity of the Bieniawski and Birchtree data.Other factors that may affect stability and exposure time were also discussed.This research has led to the development of a new empirical design method that incorporates exposure time in the prediction of open stope stability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 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".