Developments in slurry flow modelling in a historical perspective
Bibliographic record
Abstract
Abstract Durand's slurry flow model attempted to find a single formula for sand‐size particles and larger, but Babcock's data showed that a single formula could not apply. A subsequent group of models is based on Wilson's layered force‐balance analysis of slurry flows applied to friction losses and deposition limit. Models based on variants of this analysis include those by Shook and others at the Saskatchewan Research Council (SRC). Early versions of Wilson's model relied on particle fall velocity to find friction losses, but the classical iterative method of finding fall velocity has now been replaced by a direct method. This is based on the shear Reynolds number of the particle, which can be expressed in terms of the better known Archimedes number. Thus, calculations of slurry friction and limit of deposition involve two principal parameters: the Archimedes number and the diameter ratio d/D.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".