Environmental impact improvements due to introducing automation into underground copper mines
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
A life-cycle assessment (LCA) model was developed to comparatively analyze the use of manual and automated mining equipment in underground copper mine sites. Processes and key variables that were determined to contribute to the environmental impact of operations were identified for six mine sites in a range of geographical locations around the world. Our model successfully calculated carbon dioxide (CO2 eq.) emissions to within 4.9% of the reported annual emissions from the site’s respective companies. The implementation of automation was found to decrease global warming potential by a range of 11.4%–18.0% or 3.9–17.9 kg CO2 eq./t ore. The model was also used to estimate the average reductions across several impact potentials including, acidification (11.9%–17.8%), eutrophication (7.6%–13.7%), and human toxicity (16.0%–20.0%). World-wide the mining industry is moving toward introducing significantly more automation to enhance productivity and safety. This novel work demonstrates an important third dimension that can support this move, reduced environmental impact.
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