Towards continuous bulk production from below 2.5 km
Bibliographic record
Abstract
Current underground mining practice consists of a series of discrete tasks and subtasks, rather than a continuous process. These discrete tasks involve a great deal of human intervention including equipment relocation and equipment maintenance and repair. As mining becomes deeper, the intervention of human intelligence will remain essential to the process, and environmental conditions will make physical intervention prohibitively expensive and undesirable. Despite progress in automation and tele-operation, the prospect of a fully man-less operation underground seems as remote as ever. The Centre for Excellence in Mining Innovation (CEMI) believes that the problem lies not with automation, but with the design of the activities that have been automated. Industry has chosen to use automation simply to eliminate people from the activities and has left the equipment, the tasks they perform, and the process in which they are engaged all but unchanged. The CEMI approach is to redesign the individual activities in the production process so they can be managed as a series of simple, linked activities in a semi-continuous production system, made possible with the advent of underground wireless communication systems capable of conveying large amounts of data at low cost. We address the kinds of changes that must be made if we are to approach a much more cost-effective, semi-continuous ore production process with 100% utilisation of the face and maximum productive utilisation of the stope.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".