Use of Particle Tracking Velocimetry for Measurements of Granular Flows: Review and Application
Bibliographic record
Abstract
A brief review of publications that deal with particle image velocimetry (PIV) and particle tracking velocimetry (PTV) is presented first in this paper. It is followed by a brief review of papers that discuss the applications of PIV and PTV to granular flows. Next, the application of PTV to granular flows is demonstrated in the context of an experimental investigation of free-surface flows of almost spherical, slightly polydisperse, ceramic particles immersed in air. Flows of this granular material down the upper inclined surface of a wedge-shaped static pile of the same material, formed naturally and contained in a narrow channel between two parallel vertical glass plates are considered. Some sample results obtained from PTV measurements of these flows in the statistically steady and fully developed region are presented.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".