Runout estimates and risk-informed decision making for bench scale open pit slope failures
Bibliographic record
Abstract
Bench scale open pit slope failures are common occurrences in open pit mines and present a hazard to workers near freshly excavated faces. Objectively forecasting the zone of influence of bench scale failures is an important component of the mine’s risk management plan, as they occur more frequently than multi-bench scale landslides and can be more difficult to monitor. This paper presents a dataset of 167 bench scale open pit slope failures and tests runout and bench-width sizing methods to identify appropriate tools to estimate a stand-off distance from a fresh bench face. A length versus fall height and a Fahrböschung angle versus volume relationship calibrated to bench scale open pit slope failures provide reasonable runout estimates and are useful for decision-making when workers are near freshly excavated bench faces. Bench scale open pit failures appear to have a relatively constant fall height/horizontal runout distance (H/L) ratio up to 10 000 m 3 , after which H/L becomes inversely proportional to volume.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".