An update to the strain-based approach to pit wall failure prediction, and a justification for slope monitoring
Bibliographic record
Abstract
Effective management and mitigation of pit slope instability in open pit mines begins with a comprehensive slope monitoring programme. The ability to differentiate between non-critical pit wall movements due to rebound or relaxation of the excavated slopes and movements that may be indicative of slope failure is important for maintaining a safe working environment and maximizing production. Slope failure prediction methods using velocity, acceleration, and strain criteria have been introduced and put into practice over the past few decades. Forty-eight slope failures with surface monitoring data are presented and assessed using the strain-based failure prediction approach, with consideration of the quality of the rock mass and the potential failure mechanism. The results indicate that the strain-based approach can be used to provide general guidance regarding strain thresholds for pit walls for a variety of failure modes in diverse geological environments. The advantages and potential drawbacks of the strain-based and other slope failure prediction methods are discussed. The importance of implementing a pit slope monitoring and performance evaluation system early in mine development is also emphasized.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".