Bibliographic record
Abstract
The rare earths boom benefitted and ultimately suffered from the added spice of political sensitivity over supply concentration in China, the ascendance of rare earths-consuming green energy applications and some well publicised investment scams in which fraudsters coldcalled members of the public in the UK and the US offering to sell buckets of the minerals as a financial speculation. A lot of companies want to keep their hand in the rare earths game, for if and when the up-cycle comes round again, one Canada-based analyst, who preferred not to be named, told IM. Lots have done the same with lithium, graphite and vanadium - in many cases this has paid off. Every dog has its day. Rare earths prices appear to have hit bottom, wrote KC Chang, a Toronto, Canada-based senior economist at IHS Global Insight. [We] forecast prices to rise slowly over the next 24 months. The current demand environment remains quiet, but production cutbacks and industry consolidation limits further downside price risk, Chang continued. Stronger demand, accompanied with inventory restocking, could quickly tighten markets and send prices higher in 2017.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".