Discovery, supply and demand: From Metals of Antiquity to critical metals <sup/>
Bibliographic record
Abstract
Transformational growth amongst the various critical metals’ markets would reduce supply concerns for industrial consumers and governments, whilst also providing commercial opportunities for the upstream industry. However, despite rapid market growth amongst some critical metal markets over the last decade, as a group they have lagged the market growth rates of the non-ferrous industrial and precious metals sectors. Research into the growth prospects of the critical metal markets is clearly required; however, their limited economic history and a paucity of data make this difficult. The economic history of the metals and mining industry as a whole, however, is better documented, and thus may provide insights into the potential for market growth amongst the critical metals. This paper therefore reviews the economic history of metals and mining, and in particular, that of the aluminium, nickel and uranium industries in an attempt to understand the key drivers behind transformational growth within the metals’ markets. This historical review suggests that a combination of breakthroughs in discovery, supply and demand are required to catalyse transformational market growth; and thus that parties seeking to benefit from the transformational growth of the critical metals’ markets must approach these markets in an integrated manner, considering each of the discovery, supply and demand issues in turn, rather than focusing on one specific constraint.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".