Environmental impact improvements due to introducing automation into underground copper mines
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".