Quantification of the morphology of gold grains in 3D using X-ray microscopy and SEM photogrammetry
Bibliographic record
Abstract
ABSTRACT The shape of gold is widely used in mineral exploration and in sedimentology to estimate the distance of transport from the source to the site of deposition. However, estimation of the morphology is based on qualitative observations or on the quantification of shape in 2D. The 3D analysis of grain shape is useful for accurate morphometric quantification and to evaluate its volume, which is related to particle size. This study compares X-ray 3D microscope and 3D SEM photogrammetry to reconstruct the shape of gold particles. These new methods are exploited to quantify the shape of gold grains 85 to 300 μm in size. The shape parameters, such as axial lengths, surface area, volume, diameter of curvature of all corners, and diameter of the largest inscribed sphere and smallest circumscribed sphere are measured on a particle in order to estimate shape factors such as flatness ratios, shape indices, sphericity, and roundness. Most shape parameters and shape factors estimated on the same gold grain with simple geometry are similar between the two approaches. This result validates these methods for the 3D description of gold particles with simple morphology, while providing a methodology for describing grains with more complex geometry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".