The crystal-chemistry of riebeckite, ideally Na2Fe<sub>3</sub><sup>2+</sup> Fe<sub>2</sub><sup>3+</sup>Si8O22(OH)2: a multi-technique study
Bibliographic record
Abstract
Abstract In this work we report on a complete crystal-chemical characterization of a near end-member riebeckite from Malawi, and use the available data to critically compare information obtained from different analytical methods. The sample occurs as well-formed and very large single crystals in pegmatitic rocks. Accurate site-populations were determined by combining single-crystal structure refinement and electron microprobe analysis (EMPA). The Fe3+/Fe2+ ratio was obtained from Mössbauer spectroscopy. Lithium was quantified by Laser Ablation Inductively Coupled Plasma Mass Spectroscopy (LA-ICP-MS). Fourier-Transform Infrared (FTIR) spectra, collected both on powders and single crystals, are presented and discussed. FTIR spectra in the NIR region are also presented for the first time for this amphibole. The FTIR data are compatible with complete local ordering of A cations close to F, and complete Fe2+/Mg disorder at M(1,3). Polarized Raman-scattering data collected from single crystals confirm this conclusion. In addition, it was found that FTIR data collected on powders provide the best agreement with the site occupancies derived from chemical (EMPA and LA-ICP-MS) and crystal-chemical data, possibly because they do not depend on experimental issues such as orientation and polarization.
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 | 0.000 | 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 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".