Rare Metal (RM) and Rare Earth Element (REE) Resources: World Scenario with Special Reference to India
Bibliographic record
Abstract
Abstract The RM (Li, Be, Ti, Zr, Nb, Ta, Th and U) and REE (Light Rare Earths and Heavy Rare Earths including Yttrium) are strategic and critical for sustaining a variety of industries such as nuclear, defence, information technology (IT) and green energy options (wind, solar, electric vehicles and others). The 2010 ‘Rare Earth’ crisis of the world, following China’s monopoly with over 80% share and export restrictions in the REE market, led to an exploration boom for REE all over the world including India. This led to a substantial increase in REE mineral resources (98 Mt of contained REO in 2015) outside China located in Canada (38 Mt), Greenland (39 Mt) and Africa (10.3 Mt) that represents a five-fold increase in resources (c.f. Paulick and Machacek, 2017). As per the 2019, USGS commodity survey, the world reserves of REE have been estimated at 120 Mt in countries such as China (44Mt), Brazil (22Mt), Vietnam (22 Mt), Russia (12 Mt), India (6.9 Mt) and others (13 Mt). At present world resources of RM and REE are adequate to cater the demands of the different industries. The constraints, however, appear to be not technical but mainly environmental and social issues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".