Bibliographic record
Abstract
Correlations of minimum spouting velocity, with one or two exceptions, have been reported only for particles of uniform size. If spouting techniques are to be applied to fluid-solid contacting operations other than the drying of grains, the effect of particle size distribution must also be known. In this work, spouting characteristics of a variety of materials over wide and narrow particle size distributions have been studied in a 6-inch diameter column fitted with a 60° conical bottom. Air inlets used were of a special design which resulted in improved spoutability of materials and inlet orifice varied in size from 3/8-inch to 3/4-inch. Mean particle diameters were varied from 0.0134 to 0.10U inches, solids density from 65.8 to 246.3 lb.[subscript]m/ft.3 and static bed heights from 7.5 to 40 inches. The minimum spouting- velocity for all the runs has been correlated to within ± 10% by using the arithmetic mean Tyler screen size for individual fractions of particulate materials] by assuming a geometric mean particle diameter as the characteristic diameter for individual grains of granular material and the length mean diameter as the representative diameter for mixtures of all materials. Some qualitative measurements of solids attrition rates were also made.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".