Runout of open pit slope failures: an update
Bibliographic record
Abstract
A Fahrböschung angle versus volume methodology for estimating the runout distance of large open pit slope failures was proposed to the open pit geotechnics community in 2015 (Whittall et al. 2015). Since that 2015 publication, three commonly received questions on the methodology include: This paper provides a summary of refinements to runout estimate techniques for pit slope failures. In particular, empirical tools for estimating small-volume bench scale failures and a probabilistic framework for spatially distributed runout estimates are provided. A risk assessment methodology is proposed that can be mapped across the pit floor by dividing it into square grid cells and calculating individual risk for each cell. The goal of this paper is to supply practitioners with quick, repeatable tools to better understand the landslide risk to which workers and equipment are exposed, and support decision-making when working in an open pit with a developing or imminent pit slope failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".