Analysis of a database of open pit mine slope failures to predict travel distance, setback distance, and geometric properties
Bibliographic record
Abstract
In mining, open pits have slopes cut as steep as possible to ensure efficient mining yet not too steep to jeopardize safety. Building on existing contributions, the Texas A&M University Mine Slope database (TAMU-MineSlope) was created to provide global-scale insights into the runout mechanics of open pit slope failures and offer the database to engineers for further study. The database includes 134 cases of open pit slope failures that occurred at 76 mines worldwide. Based on energy principles and an analysis of the TAMU-MineSlope data, an equation to predict the travel distance of the slope failure mass is presented in this paper. Furthermore, based on the case histories analysis, an equation to predict the setback distance is proposed. Finally, the TAMU-MineSlope database shows that the average width to height ratio of the failing slope mass is 2.7 and that, for this ratio, the 3D factor of safety (FS) can reach a value 15% higher than the 2D FS. The database has been released in the public domain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".