Rietveld refinement of LaB<sub>6</sub>: data from µXRD
Bibliographic record
Abstract
The Rietveld method of crystal structure refinement was an important breakthrough, allowing crystal structural information to be obtained from powder diffraction data. One remaining challenge is to collect Rietveld-quality data for polycrystalline mineralsin situ, using laboratory-based micro X-ray diffraction (µXRD) techniques. Here a new data collection method is presented, called `multiframes', which produces high-quality data, suitable for Rietveld refinement, using the Bruker D8 DISCOVER micro X-ray diffractometer. 91 frames of two-dimensional X-ray diffraction data were collected for powdered NIST SRM 660 LaB6standard material, using a general area-detector diffraction system (GADDS), at intervals of 0.8° 2θ. For each frame, only the central 1° 2θ was integrated and merged to produce a diffraction profile from 17 to 90° 2θ. Rietveld refinement of this data usingTOPAS2gave a unit-cell parameter (ao) and atomic position of boron (x) for LaB6of 4.1549 (1) Å and 0.1991 (9), respectively (Rwp= 4.26,RBragg= 3.21). The corresponding La—B bond length was calculated to be 3.0522 Å. These parameters are in good agreement with the literature values for LaB6. These encouraging results suggest that Rietveld-quality micro X-ray diffraction data can be collected from the Bruker D8 DISCOVER diffractometer, provided that the GADDS detector is stepped in small increments, for each frame only the central 1° 2θ is integrated at constant arc length, and counting time is sufficient to yield adequate intensity (∼10 000 counts).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.029 |
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".