Bibliographic record
Abstract
This introductory chapter follows the contours, and in some cases even the exact wording, of Chapter 1 in Spouted Beds , the only book prior to the present publication that deals exclusively with this subject. Indeed, the current venture was originally to be a revised version of that 1974 book by the present editors. However, after writing the first draft of this chapter for the revision, we realized that the breadth and variety of work on spouted and spout-fluid beds since 1974 required input from a wide range of authors for coverage to be completed in a finite time. Changes in the subsequent draft were mainly with respect to layout of chapter topics (Section 1.6). Despite advances since 1974, the earlier book of that year remains a repository of useful information not available in this volume or elsewhere. The spouted bed Consider a vessel open at the top and filled with relatively coarse particulate solids. Suppose fluid is injected vertically through a centrally located small opening at the base of the vessel. If the fluid injection rate is high enough, the resulting high-velocity jet causes a stream of particles to rise rapidly in a hollowed central core within the bed of solids. These particles, after being carried somewhat beyond the peripheral bed level, rain back onto the annular region between the hollowed core and the column wall, where they slowly travel downward and, to some extent, inward as a loosely packed bed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.376 | 0.244 |
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".