Development of Echo-LPT for the study of particle-wall interactions in dense suspensions
Bibliographic record
Abstract
Examining the behaviour of dense suspensions has proven to be difficult, both experimentally and numerically. Using super water–absorbent polymer, PIV measurement was successfully conducted in a hydrogel suspension with a volume fraction (VF) of Φ =20% (see Zhang and Rival, 2018). However, due to the slightly refractive index mismatch, the image quality will degrade significantly as the particle loading of the hydrogel is increased. In order to achieve flow measurements in suspensions with high volume fractions, non-optical based techniques such as ultrasound imaging velocimetry (UIV) should be implemented. UIV has been developed for fluid dynamics applications and embraced by many researchers to study fluid flows (Gurung and Poelma, 2016; Jeronimo et al., 2019). Although, UIV provides useful information about the flow physics, it is unable to provide Lagrangian quantities such as particle trajectories, which is a key parameter to study entrainment and particle-wall interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".