Digital Twins and Enabling Technology Applications in Mining: Research Trends, Opportunities, and Challenges
Bibliographic record
Abstract
Industry 4.0 is making a positive impact on the world’s industries in areas such as productivity, efficiency, reliability, and human safety. Although the mining industry has not undergone revolutionary digital change, numerous technologies have been applied and improved during the past two decades. Through a systematic literature survey, this study presents research trends, opportunities, and challenges in the context of digital twinning in the mining industry. The research team initially set the objectives of the research and formulated the search criteria to extract the most relevant and manageable number of scientific peer-reviewed publications. The gathered publications were then filtered using a web-based text reading and analysis environment based on defined criteria. The filtrate of the first step was then refined by manually reading through abstracts, introductions, and conclusions while classifying relevant publications based on aspects such as the country of origin, technologies, and application areas. The filtrate of the second step was subjected to detailed manual reading to further explore the technologies and applications to capture the research trends, opportunities, and challenges. The research outcomes indicate that China, the USA, Australia, Russia, and Canada are the leading countries in this research context. Creating a safer mining environment using virtual reality is popular among other applications and technologies. In the last twenty years, academic institutions and scholars have led research efforts compared to the industry. Over the past five years, both have significantly increased their research contributions, presenting various opportunities and challenges to inspire future studies.
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.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".