Recent Progress in the Viscosity Modeling of Concentrated Suspensions of Unimodal Hard Spheres
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The viscosity models for concentrated suspensions of unimodal hard spheres published in the twenty-first century are reviewed, compared, and evaluated using a large pool of available experimental data. The Pal viscosity model for unimodal suspensions is the best available model in that the predictions of this model agree very well with the low (zero)-shear experimental relative viscosity data for coarse suspensions, nanosuspensions, and coarse suspensions thickened by starch nanoparticles. The average percentage error in model predictions is less than 6.5%. Finally, the viscous behavior of concentrated multimodal suspensions is simulated using the Pal model for unimodal suspensions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it