Determination of optimum drawpoint layout in block caving using sequential Gaussian simulation
Bibliographic record
Abstract
The economics of today’s mining industry are such that the major mining companies are increasing the use of massive mining methods. Caving methods have become the underground bulk mining method of choice and are expected to continue as such in the foreseeable future. Caving methods are favoured because of their low cost and high production rates. They offer a much smaller environmental footprint compared to equivalent open pit operations due to the much smaller volume of waste to be moved and handled. Drawpoint spacing is an essential part of the block cave layout design which must be investigated carefully at the start of the project. In this paper, an overall methodology based on sequential Gaussian simulation (SGS) to obtain the drawpoint spacing is suggested. The optimised drawpoint spacing is used to maximise the profit since the extraction layout is highly essential for the economics of block caving. This study is opening a new horizon for using ‘all realisations all the time’ as a new approach to solve one of the trickiest elements of blocks caving.
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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".