Life cycle assessment to demonstrate how automation improves the sustainability performance of an underground mining operation
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 worldwide move to introduce more automation into underground metal ore mining is currently aimed at improving both operational productivity and safety. We have used a comparative life cycle assessment (LCA) as a novel approach for the industry to determine the beneficial impacts automation can also have on environmental performance using data collected on mine site productivity and energy consumption. The LCA looked at four impact categories: global warming potential, acidification, eutrophication, and human toxicity. When comparing key automated equipment to their traditional manual counterpart, all four impact categories experienced a reduction with automation and a subsequent improvement in sustainability performance. Global warming potential, for example, decreased by 18.3% over the mine life period, or 3.7 kg of carbon dioxide equivalent (CO2 eq.) per tonne of ore extracted. Environmental impact reductions were due primarily to lower diesel fuel consumption in the loading and haulage processes as well as a 27% shorter operational mine life leading to less years of mine and mine camp maintenance.
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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 | 0.001 | 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 | 0.001 |
| 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