Bibliographic record
Abstract
Open pit mining projects usually face particular situations while dealing with slope stability analysis.More than any other projects, mining projects build a considerable number of slopes, which are more likely to behave differently among them, due to the change in orientation in the open pit.One of the situations that represent a considerable problem is the presence of a weak layer, which could be easily analyzed by using the limit equilibrium methods.Nonetheless, there are some practical measures that mining engineers take in order to improve the slope stability condition of mining slopes involving a weak layer, which consist in creating a disturbance in the rock medium surrounding the weak layer by blasting a strip of the rock mass.That modification of the rock medium is not easy to analyze with traditional limit equilibrium methods, because there is not a constitutive model to properly characterize that portion of the disturbed medium.This paper presents a first approach to analyze the stability in open pit mining slopes in the presence of a weak layer, both, before and after blasting to create a disturbance of the medium.This approach considers the effect of the rock mass disturbance, by using a combination between data coming from inclinometer monitoring in the slopes and numerical simulations with finite elements, which allows to monitor the slope stability during the rock mass disturbance.The disturbed rock mass, known as "bimrock", is then characterized and included into the slope stability models to obtain the factor of safety considering the disturbance of the medium.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".