The Redox Behavior of Rare Earth Elements
Bibliographic record
Abstract
Elements in the lanthanides series are usually known under the name of rare earth elements (REE). In past decades, the use of these elements in industrial processes has increased exponentially with the development of novel technologies and applications. In addition to such growing industrial importance, and despite their relatively low concentrations in the Earth system, REE are sensitive markers of crystal-melt equilibria in geological systems. As a result, the study of lanthanides in materials, either as whole group or as single species, has become an important point of focus in both Geo- and Materials Sciences. Among REE, Ce and Eu are stable in many geological environments, with two different oxidation states (Ce 3+ /Ce 4+ and Eu 2+ /Eu 3+ ), hence, these two redox couples have been proposed as proxies for oxygen fugacity ( f O 2 ) in different environments (oxybarometer). However, it is essential to understand the factors influencing redox mechanisms in order to use Ce/Eu distribution as f O 2 sensors. In this chapter, we present an overview of REE properties in minerals, melts, and glasses, and illustrate their importance through examples of applications in the fields of geochemistry, petrology, and mineralogy.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".