Comparison of Laboratory Diffraction Contrast Tomography and Electron Backscatter Diffraction Results: Application to Naturally Occurring Chromites
Bibliographic record
Abstract
Understanding how minerals are spatially distributed within natural materials and their textures is indispensable to understanding the fundamental processes of how these materials form and how they will behave from a mining engineering perspective. In the past few years, laboratory diffraction contrast tomography (LabDCT) has emerged as a nondestructive technique for 3D mapping of crystallographic orientations in polycrystalline samples. In this study, we demonstrate the application of LabDCT on both chromite sand and a complex chromitite sample from the Merensky Reef (Bushveld Complex, South Africa). Both samples were scanned using LabDCT and Electron Backscatter Diffraction (EBSD), and the obtained results were rigorously evaluated using a comprehensive set of qualitative and quantitative characterization techniques. The quality of LabDCT results was accessed by using the "completeness" value, while the inaccuracies were thoroughly discussed, along with proposed potential solutions. The results indicate that the grain orientations obtained from LabDCT are comparable to that of 2D EBSD but have the advantage of collecting true 3D size, shape, and textural information. This study highlights the significant contribution of LabDCT in the understanding of complex rock materials from an earth science perspective, particularly in characterizing mineral texture and crystallography in 3D.
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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 | 0.001 | 0.002 |
| 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".