Bibliographic record
Abstract
The rare-earth elements include Sc, Y and the 15 lanthanoids from La to Lu. Owing, in part, to their diverse coordination numbers and geometries and relatively localized orbitals, the rare-earth materials find application in lighting, displays, catalysis, hydrogen storage, photovoltaics, magnetism, magnetocalorics, thermoelectrics, biomedical science and sensing, amongst others. In addition to their various applications, there are diverse classes of materials that can be synthesized using rare-earth elements including coordination complexes, polymers, metal–organic frameworks (MOFs), solid-state inorganic materials, and nanoparticles, etc.1 Therefore, in addition to the structural diversity of rare-earth materials, the unique electronic properties of the rare-earth elements, particularly the lanthanoids, engenders the resulting materials with tunable luminescence, and magnetic behaviour, etc.1,2 This themed collection showcases both the structural diversity and versatile physical properties of rare-earth materials. Here we have chosen several papers from this themed collection to feature, while there are other papers in the collection that advance the materials chemistry of rare-earth elements, and we believe that all papers will be of high interest to the community.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.037 |
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".