Electron backscatter diffraction analysis and orientation mapping of monazite
Bibliographic record
Abstract
Abstract Electron backscatter diffraction (EBSD) analysis of monazite requires a comparison of empirically collected electron backscatter patterns (EBSPs) with theoretical diffraction data, or ‘match units’, derived from known crystallographic parameters. Published crystallographic data derived from compositionally varying natural and synthetic monazite are used to calculate ten different match units for monazite. These match units are used to systematically index EBSPs obtained from four natural monazite samples with different compositions. Analyses of EBSD data, derived from the indexing of five and six diffraction bands using each of the ten match units for 10,000 EBSPs from each of the four samples, indicate a large variation in the ability of the different match units to correctly index the different natural samples. However, the use of match units derived from either synthetic Gd or Eu monazite crystallographic data yield good results for three of the four analysed monazites. Comparison of sample composition with published monazite compositions indicates that these match units are likely to yield good results for the EBSD analysis of metamorphic monazite. The results provide a clear strategy for optimizing the acquisition and analysis of EBSD data from monazite but also indicate the need for the collection of new crystallographic structure data and the subsequent generation of more appropriate match units for natural monazite.
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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 | 0.000 |
| 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".