Bibliographic record
Abstract
Most underground mines produce a significant quantity of development waste rock in order to access the orebody and provide underground excavations for mining infrastructure. Much of this development waste rock is hauled to surface in many mines and results in a substantial operating cost. In addition, most mines use cemented backfill to fill the stope voids that are not filled with waste rock, and this cemented backfill represents a substantial operating cost as well. Some mines do dispose of some development waste rock in stopes, however, the operational difficulties associated with disposing of a large quantity of rock in the stopes prevents many mines from taking advantage of this option. One of the principal difficulties is the placement of backfill and waste rock in the right proportions so that there are no areas of waste rock only, i.e. rock that has no binding matrix to hold the rock particles together. Traditional methods of waste rock disposal in backfill stopes using scoops that dump their buckets over the brow have been severely limited by this constraint and resulted in limitations on the rate of waste rock delivery into the stope, as well as the maximum amount of rock that can be placed into the stope. Delivery of waste rock from the development face to the stope can also be challenging if the distance is substantial. Scheduling of the development waste rock disposal in the available stopes can be difficult since the options for waste rock storage are typically limited on an active mining level and the peaks in development rock production may exceed the capacity of a stope to receive development waste rock. This paper is an adaptation of a previous paper prepared for the 20th International Seminar on Paste and Thickened Tailings, held in June 2017 in Beijing, and discusses procedures, methods and equipment that can be used to allow waste rock to be delivered more effectively to the stopes to lower a mine’s operating costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".