Representative Elementary Volume Analysis of Sands Using X-Ray Computed Tomography
Bibliographic record
Abstract
Abstract The concept of a representative elementary volume (REV) provides an effective means of developing macroscopic measures in the description of granular materials. However, due to the difficulties associated with the measurement and characterization of granular microstructure the existence and size of an REV has remained largely conjectural. This study presents a systematic method to examine the characteristics of the REV using X-ray computed tomography images. The 3-D images of spherical glass beads, Silica sand, and Ottawa sand have been characterized using advanced image processing techniques. An interactive computer program is developed to study porosity variation within a sphere with increasing radius from the images of these materials. The porosity variation of Silica sand and Ottawa sand showed three characteristic regions: an initial fluctuation region due to microscopic variations, a constant plateau region, and a region with a monotonic increase/decrease due to heterogeneity. The homogenous medium of glass beads did not show the last region. The results show that for a random packing of spherical glass beads the REV is about two to three times of the identical average diameter. The radius for Silica sand composed mainly of elongated particles is between 5 to 11 times of d50 and for Ottawa sand composed mainly of subrounded particles is between 9 to 16 times of d50.
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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 | 0.000 |
| 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".