The Economic Geology of Scandium, the Runt of the Rare Earth Element Litter
Bibliographic record
Abstract
Scandium is currently in high demand because of a number of technological advances in the aerospace and automotive sectors of the global economy.In this paper, we review the properties of scandium, the geology of the major economic and potentially economic scandium deposits and the processes that may concentrate scandium to exploitable levels.We also show that, although scandium is classified as a rare earth element (REE), it behaves very differently from the rest of its family.The reason for this is that it has an ionic radius very similar to that of iron and magnesium and consequently concentrates easily in major ferromagnesian rock-forming minerals, notably clinopyroxene.Unlike the other REE, it is therefore a compatible element.In many scandium deposits, clinopyroxene is the main ore mineral, although in some deposits, scandium is hosted by minerals that also concentrate the other REE.As is the case for these other REE, the main source of scandium is the mantle and the conveyors of scandium are alkaline igneous rocks (and carbonatites) including Alaskan-type ultramafic rocks.The main magmatic processes involved in scandium concentration are partial melting and fractional crystallization.We model the fractional crystallization of clinopyroxene to predict the scandium content of Alaskan-type ultramafic rocks, and use this information in conjunction with a simple model of fluid-assisted partial melting to explain the genesis of scandium-rich pegmatites.In addition to magmatic processes, aqueous fluids may play an important or even essential role in scandium ore formation.The lack of reliable high temperature thermodynamic data for the aqueous scandium species precludes modeling their transport in hydrothermal fluids.However, the availability of ambient temperature data allowed us to model scandium concentration by rainwater in laterite developed above an Alaskan-type ultramafic complex.This review is no more than an introduction to the economic geology of scandium and the processes that appear to be responsible for the genesis of scandium ores, but one, which we hope will provide a guide to future in-depth studies of scandium deposits and strategies for their successful exploration and exploitation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".