Historical Trends in Base Metal Mining: Backcasting to Understand the Sustainability of Mining
Bibliographic record
Abstract
base metal mining sector has been and continues to remain an important endeavour in many parts of the world. mining of copper, lead-zinc-silver and nickel has led to social and economic development but has also left significant and sometimes lasting environmental impacts. Metal mining is widely perceived to be unsustainable, often without question, since it is drawing down natural capital (ie. stock) - despite the productive output of mines now being considerably larger than at any time in history. What has underpinned this paradox ? and what are the long term trends in base metal mining ? and how can this knowledge be used to understand the current position and future challenges of base metal mining ? This paper will present long-term data on trends in base metal mining such as production, ore grades and economic resources, focussing on major mining countries such as Australia, Canada and the United States. By looking back to history, or 'backcasting' in sustainability language, we can better understand the historical challenges, patterns or factors that have shaped the base metal mining industry. This then helps us to understand the current and future challenges facing the base metals sector of the global mining industry. An oft-quoted saying for peak oil pundits is that of Saudi's former Oil Minister Ahmed Zaki Yamani: The Stone Age came to an end not for the lack of stones and the oil age will end, but not for the lack of oil. Will the base metals mining sector go the same way ? That is, what has been the success rate for new technology, exploration, economics or perhaps environmental aspects which may constrain the sector or, conversely, help it to flourish ? This paper presents a unique insight into base metals over time, and provides a range of valuable but rarely compiled historical data.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".