A comparison between Offset Herringbone and El Teniente underground cave mining extraction layouts using a discrete event simulation technique
Bibliographic record
Abstract
Several underground cave mining operations have realised benefits in development rates when an extraction level layout is designed according to the El Teniente style. Geotechnical benefits may accrue in adopting the advance undercut technique in which extraction drift development lags the undercut development directly above. However, few technical studies are available in the literature that directly compare the Offset Herringbone and El Teniente styles when the decision criteria focus is on development rate impacts. This paper reports on research into discrete event simulation (DES) modelling that emulates stochastically the process of building the interdependent lateral infrastructure levels within the footprint of an existing cave mining method that uses the advance undercut technique. Such DES models can capture design considerations: firstly in the early stages of development, in which a limited number of headings are available; and secondly in the full stages of development where the maximum number of headings are governed by the width of the ore body. DES modelling is discussed in this paper in a case study situation, where results indicated that an additional 3% of drawpoint drivage required in an El Teniente layout could increase development rates by an average of 9%.
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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 | 0.001 | 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".