How rare earths metals power our future — and the risks we must consider
Bibliographic record
Abstract
Rare earth elements (REEs) are indispensable to modern technology, driving advances in renewable energy, consumer electronics, and medical imaging through their unique magnetic, light-emitting, and catalytic properties. From the rare earths in wind turbine magnets to those in the phosphors that give LED displays their vivid colors, these 17 metals underpin critical sectors of the global economy. You might never notice them, yet they are present in almost every device around you. Their story, however, is not without complications: extraction and processing can cause deforestation, soil and water contamination, and in some regions are tied to human rights abuses. Meeting these challenges will require better recycling, greener processing, and alternative materials, which is a task that grows more urgent as demand soars. Ultimately, understanding the science, applications, and policy of REEs is key to ensuring a sustainable and equitable supply for the technological transition of the 21st century.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".