Opportunities for research to achieve the vision of the Smart Mine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it