Bibliographic record
Abstract
Empirical methods should be applied only in situations where the features are similar to those used in their original database. The Stability Graph methodologies proposed by Mathews et al. (1981) and by Potvin (1988) are used to design open stopes. Case studies of Canadian and Australian mines bear the two propositions. There is no evidence that these methodologies can be extrapolate to the geomechanical features of Brazilian mines. The main objective of this study was to find evidence supporting the use of these approaches for geomechanical Brazilian conditions. This study consisted of collecting data from Brazilian mining companies that employs sublevel stoping mining method, estimate the operational dilution, as well as the hydraulic radius and the Stability Number for each stope face. Numerical modeling was used to obtain the stability number factor related to the stresses around the excavation. The Stability Graph Methodologies proposed by Mathews et.al (1981), Potvin (1988) and Mawdesley et al. (2001) have been used to compared the results with the limits of stability defined by each author. The analysis of 65 surfaces (hangingwall, footwall, stope end, stope begin and back) of 17 stopes were used in this study. Usually the hangingwalls and the backs are more unstable and, therefore, were analyzed with greater concern. The parameters needed for this analisys may present a bias depending on the stope face observed. All walls were analyzes but hanginwalls and backs, were reviewed with greater emphasis. The restricted database does not allow to conclude that the Brazilian mines are in agreement with the methodologies evaluated, and does not support the use of different stability limits. A larger number of case studies should be used to validate the efficacy of these methods in Brazilian mines.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".