The effects of automation on the environmental impact of deep underground metal ore mining operations
Bibliographic record
Abstract
The growing demand for increased production has resulted in the need to develop deeper \nunderground mines to extract more resources. However, the mining process becomes less \neconomically attractive as the ventilation and ore transportation costs drastically increase when \noperating at large depths. This has led to the industry investigating automated battery-electric \nand biodiesel fueled machinery instead of diesel machines to reduce emissions, and hence \nventilation costs, as well improve productivity and thereby, the economic viability of deep mine \nprojects. \nA life cycle assessment (LCA) approach has been developed to evaluate the environmental \nimpact from introducing automated equipment in underground copper mines. This is a novel \napplication for an LCA, and as a gauge of model accuracy, it was found that calculated \ngreenhouse gas (GHG) emissions for an underground mine site in Canada were within 5.6% of \ntheir reported emissions. The model was then expanded using data collected from automation \ntrials at a Canadian mine to predict changes due to the introduction of various levels of \nautomation with regards to the impact potentials of global warming, acidification, eutrophication \nand human toxicity. All impact levels were quantified and found to decrease due to automation. \nData from this site study was then used to further develop the LCA model to predict changes in \nenvironmental impacts for underground copper mine sites in Australia, Canada, Poland, USA \nand Zambia. Site specific parameters and processes that contribute to their overall environmental \nimpacts were identified, and the calculated CO2 emissions were within 4.2-5.6% of the reported \nvalues. \nThe mining industry is moving toward introducing significantly more technology to enhance \nboth productivity and safety. This thesis investigates using an LCA approach to add a third \ndimension; improved environmental impacts that contribute to more sustainable mining
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".