An empirical assessment of two-dimensional (2D) Debye–Scherrer-type image-plate X-ray diffraction data collapsed into a 1D diffractogram
Bibliographic record
Abstract
X-ray diffraction (XRD) has been routinely employed in the Earth sciences to characterize the crystallography of rocks and minerals. Routine characterization of samples too small for analysis by classic automated powder diffraction methods becomes challenging without access to single crystal or micro-diffraction equipment. Here, we show that a traditional Gandolfi camera lined with an image-plate (IP) as the detection medium can return a fully quantitative diffraction pattern from a sub-milligram single grain specimen in a simple and straightforward manner. Data pertaining to peak positions ( d -spacings) were assessed using SRM640c Si powder, while intensity data were compared to the certified values for intensity standard SRM676a alumina powder. The refined unit-cell dimension of Si powder differed from the certified value of 5.4312 Å by no more than 0.0003 Å with a standard deviation ( σ ) of 0.0002 Å among the three experiments. For intensity, the σ and disparity from the certified values of three diffraction experiments on SRM676a were both <2%. The results of a comparative study of the crystallographic parameters determined for a naturally occurring garnet and clinopyroxene given through the refinement of their crystal structure by single-crystal XRD method are presented. These show through Rietveld refinement of X-ray data obtained by the Gandolfi–IP method outlined here that both accurate and precise XRD data can be produced in a timely and cost-effective manner using only an IP, Gandolfi camera, and software freely available on the internet.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".