Digital technologies for energy efficiency and decarbonization in mining
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
Several mining companies have set targets to decarbonize their operations by the year 2050. At the same time, there is pressure on the mining sector to increase the supply of minerals needed for clean energy technologies. Digital technologies such as automation, artificial intelligence, machine learning, and the Internet of Things are reshaping the way the mining sector works. This literature review identifies examples of current digital technologies implemented in mining operations and highlights their reported benefits. Although several benefits were reported, mining companies tend to focus on safety, productivity, and cost. Energy and greenhouse gas reductions are commonly overlooked, despite having the potential to shrink the mining carbon footprint. Quantifying the energy and greenhouse gas emission reductions achieved through implementation of digital technologies could strengthen the business case to enhance their adoption and help the mining sector reach decarbonization goals.
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 | 0.000 | 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