Firms advancing rare earth plans will entice investors
Bibliographic record
Abstract
Significance Disruption to mines in Asia has restricted supply while demand has been boosted by decarbonisation plans and Western efforts to reduce reliance on supply from China. European states seeking to cut their consumption of Russian hydrocarbons by accelerating the roll-out of wind power have added to the pressure. Impacts China hopes to exert more control over the market by turning its six state-owned rare-earth firms into two vertically integrated majors. Rare-earth recycling is developing: it will soon be possible to get 1.1 tonnes of REE oxide from 3.0 tonnes of decommissioned magnets. Western diversification efforts are rising: Ontario’s Critical Minerals Strategy aims to connect the province’s mines with the EV sector. Australia’s Iluka has divested from Sierra Rutile, its mineral sands project in West Africa, to focus on rare earths projects at home.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.234 | 0.075 |
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".