Bibliographic record
Abstract
Any innovation in part of the mining process has to be integrated into a production system. The mineral dressing system and the tailings management system are easily recognisable as process systems, managed by trained process engineers. The mining process, and especially the underground mining process, have somehow escaped the constraints imposed by the process engineering included in manufacturing operations and is still regarded as a series of almost separate activities – drill-and-blast, ventilation, ground support, ore transfer, and backfilling, each with its own specific expertise and practitioners. This discrete approach has been successful for relatively small-tonnage operations, but it begins to fail as the daily production demand increases. Many underground metal mines using bulk mining and fill have been successful in producing 5,000–8,000 tpd, and the same production equipment platform was adopted for block caving operations in low-grade copper porphyry operations to achieve more than 50,000 tpd. These mines are struggling to meet design targets of 100,000 tpd, at the same time that bulk mining operations are struggling to maintain their production levels with greater ventilation and logistical challenges at depth. The inability to meet current production targets has led to a series of tactical responses, such as layout changes, equipment automation and electrification, and other new technologies. We make the case that the implementation of isolated technologies into deep, hightonnage operations are unlikely to be successful unless they are integrated into a mine production system that is designed to address all the system constraints. We believe that the last technological transition has created a progress trap that will prevent mines achieving higher production rates. We believe a ‘systems approach’ to mining innovation is essential if we are to transition to technology platforms that can meet future performance targets, match demographic projections and enable the industry to meet future metal demand.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".