Runout analysis and mobility observations for large open pit slope failures
Bibliographic record
Abstract
Objectively forecasting the runout of a potential open pit slope failure, in addition to identifying the failure itself, is a critical component of a mine’s risk management plan. Recent losses arising from large open pit slope failures demonstrate shortcomings in current practice. A dataset of 105 pit slope failures was compiled to compare open pit runout trends against established empirical runout relationships for natural landslides. Fahrböschung angle versus volume and Fahrböschung angle versus slope angle relationships provide reasonable runout estimates. Open pit slopes have the advantage of removing the influence of morphological features, vegetation, and liquefiable substrates while controlling the travel path angle and roughness. In such a controlled environment, landslide mobility has a strong sensitivity to slope angle, material properties, and fall height, and is only modestly sensitive to volume. A grouping of highly mobile open pit slope cases involving weathered, saturated, collapsible rock mass materials exceed expected runout distances when compared with established runout trends. This suggests mobility for these weaker rock masses is controlled by pore pressures mediating basal friction. The result is that two different runout exceedance trends are observed based on whether the unstable rock mass involves fresh, strong rocks or weathered weak rocks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".