Potential Impact of U.S. Re-Emerging Rare Earths Industry on Future Global Supply and Demand Trend
Bibliographic record
Abstract
Recently, based on the rapid growth of internal markets and limited reserves, some REEs have been in a supply shortage, especially HREEs. Thus, how to get new REs production sites and supply chain becomes an urgent global problem. U.S. government announced the resumption of production of domestic REs ores in 2012 to respond to the increasing internal demand for and contracting supply of REs. Aiming to explore U.S. re-emerging REs industry’s potential impact on future global REs supply and demand trend, this paper reviews the current global REs supply and demand and U.S. re-emerging REs industry and then forecast and analyze the future impact of U.S. re-emerging REs industry on global REs supply and demand trend using Documentary Research Method. The result is that U.S. re-emerging REs industry will lead to a significant impact of supply surplus in total on future global and its internal REs demands. Nonetheless, the global including U.S. demand of HREEs will still rely on China’s supply. The results will provide references to the development of U.S.’s REs industry and China’s industrial policies. It will also make contribution on improving global REs supply and demand relationship.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".