Bibliographic record
Abstract
In underground mines, large quantities of waste rock can be produced during development in order to access ore bodies. The waste rock is typically hoisted to the surface and transported to a specific place to make a structure called a waste rock pile. This practice requires energy consumption and generates additional operating costs for transporting waste rock from underground to the surface. Alternatively, the waste rock can be poured directly into underground mine stopes filled with paste backfill. As a result, energy consumption and additional operating costs for transporting the waste rock from underground to the surface are avoided or significantly reduced. However, the natural mixing behaviour of waste rocks poured in paste backfill has never been studied. The fill mass generated by this practice can fail and collapse upon a side-exposure associated with the excavation of an adjacent stope if a poor mixture between the cohesionless waste rocks and cemented paste backfill takes place around the exposed face. Thus, it is critical to understand the mixing behaviour of waste rocks poured in paste backfill. To this end, a series of physical model tests have been performed in the laboratory. The results, in part, are presented and discussed in this paper.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".