Opportunities for research to achieve the vision of the Smart Mine
Bibliographic record
Abstract
The mining industry is being shaped by ongoing digital transformation, leading to the Smart Mine. This article aims to clarify this concept for underground extraction operations as expressed by mining practitioners and compare this vision with recent academic work. Based on an industry-focused literature review, this paper categorizes the vision of the Smart Mine in terms of objectives, solutions, and business management processes. The framework is then used to analyze academic papers selected from a systematic literature review. Results show that mining practitioners and academics are aligned in terms of the financial, operational, business, safety, and environmental objectives of the underground Smart Mine. Multiple solutions to achieve a Smart Mine are proposed and involve infrastructure, technology, people, culture, management systems, processes, and equipment. Both academics and mining practitioners focus on equipment and technology initiatives, while people and culture are underestimated. These solutions involve various business management processes, with a greater emphasis from practitioners on environmental, social, and governance (ESG) and information and data management. However, the academic literature on business management processes is relatively sparse and mainly focuses on education and training, automation management, and ESG management. Asset management, change management, and risk and safety management should be further developed.
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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 | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".