Bibliographic record
Abstract
Two dimensional shear flows of water, oil and oil-fibre suspensions of 0.01%, 0.025% and 0.05% mass consistency were examined using particle image velocimetry (PIV). A square obstacle that block 50% of the flow area in a rectangular flow cell induced shear. PIV measurements were taken at various positions on the plane of symmetry, adjacent to the square and in the wake region. The data shows that the higher viscosity of the oil results in a much larger boundary layer thickness than that of water and reattachment to the square that does not occur with water. The data also indicates that fibre loading causes a reduction in the intensity of vorticity and velocity fluctuations at the fibre length scale. Two dimensional shear flows of water, oil and oil-fibre suspensions of 0.01%, 0.025% and 0.05% mass consistency were examined using particle image velocimetry (PIV). A square obstacle that block 50% of the flow area in a rectangular flow cell induced shear. PIV measurements were taken at various positions on the plane of symmetry, adjacent to the square and in the wake region. The data shows that the higher viscosity of the oil results in a much larger boundary layer thickness than that of water and reattachment to the square that does not occur with water. The data also indicates that fibre loading causes a reduction in the intensity of vorticity and velocity fluctuations at the fibre length scale.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".