The minerals industry in the era of digital transition: An energy-efficient and environmentally conscious approach
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 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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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 | 0.000 | 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