A fast method to find smooth economic envelopes for block and panel caving mines
Bibliographic record
Abstract
The first step in the planning of a mine is the determination of the economic envelope, which is the volume that encapsulates the mine layout and maximizes the total economic value; thus, it has a considerable impact in the design and planning of the mining project. The most common approach for calculating the economic envelope of block and panel caving mines is to determine the best envelope for each possible floor and then chose the est from manually. This method is very fast and effective but may generate footprints that are difficult to design and operate as they do not consider any geometrical constraint of the shape of the envelope that are relevant, for example, to limit abutment stress. In this paper, we present a novel method that incorporates geometrical constraints related to the connectivity and smoothness of the outline of the envelope, and the continuity and smoothness of the height of the draw columns. Our method is based on the same approach used to compute the ultimate pit, i.e., the economic envelope of open pit mines . Therefore, it is very efficient and ensures to find the optimal solution for the constraints that are set. We test the method, comparing the economic envelopes of the de facto approach and our proposed approach and show that the geometries obtained are more favorable geometries and generate economic values similar to or better than those calculated using the standard approach. That is, the method improves the geometry of the economic envelope without loss in value.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".