Can we study particle breakage in tomography images
Bibliographic record
Abstract
The impact of particle breakage on the particle size distribution (PSD) of granular materials is a key element in many engineering applications. High-resolution micro-computed tomography has demonstrated that particle breakage can be observed under various conditions; however, many available methods lack continuous tracking. In this work, a novel and consistent image analysis pipeline, named Piece-by-Piece, is introduced to track fragmented particles from the onset of breakage through later stages. The goal is to provide a reliable tool, which is independent of the experiment analysed. Two different study cases are examined: a triaxial test on Ottawa sand particles and a one-dimensional compression test on zeolite granules. In the Ottawa sand test, breakage is successfully tracked throughout the entire experiment with a negligible loss of volume. In contrast, for the Zeolite granules the analysis is terminated before the conclusion of the test, although this does not suggest any deficiency in the tracking algorithm, but rather limitations in image resolution. These results show that continuous particle tracking is possible and serve as a showcase for the enhanced capabilities of Piece-by-Piece, which could offer valuable insights into micro-scale breakage mechanics.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".