The minerals industry in the era of digital transition: An energy-efficient and environmentally conscious approach
Bibliographic record
Abstract
The concept of the 4th industrial revolution is becoming a strategic determinant of sustainability, success and competitiveness in the modern mining sector. The importance of digital transformation in the mining industry has long been debated, hampered in part by the conservative nature of the mining sector. Much of the debate has focused on choosing suitable mining techniques that provide acceptable levels of ore/waste selectivity, the scale of implementation, cost reduction and suitable metallurgical extraction techniques. The purpose of this review is to give an overview of the digital transformation of the minerals and extractive industry with a focus towards energy efficiency and environmental sustainability. We address: (a) geological elements that influence the level of selectivity during mining, and technologies that deal with waste rejection; (b) eco-friendly techniques, such as tunnel-boring machines, or the use of non-explosive techniques that can assist fragmentation of ores, thereby decreasing energy requirements during mineral processing and improving mineral recovery; (c) use of low-water-consumption automated ore-waste sorting systems; (d) selective metal leaching using coarse particle percolation as an alternate method for treating complicated low-grade ores; and (e) assessing new technological boundaries for the mineral sector. A combination of these aforementioned processes will significantly reduce mining waste. Orebody features, mining methods and equipment, desired scales of implementation, alignment with circular strategies, ore extraction efficiency, and socio-economic factors all play a role in the development and implementation of new technologies and techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".