Bibliographic record
Abstract
Spouted beds have now been studied and applied for more than 50 years; during this period there has been a continual output of research papers in the engineering literature, considerable efforts to apply spouted beds in agriculture-related and industrial operations, and five international symposia dedicated solely to spouted beds. The book Spouted Beds by Kishan Mathur and the first-named editor of this volume summarized the field up to 1974. Since then there have been several reviews, but none that have surveyed the entire field comprehensively, including aspects that were barely touched in the earlier book or that were entirely absent. Examples of new areas include mechanically assisted spouting, slot-rectangular spouted beds, spouted and spout-fluid bed gasifiers, spouted bed electrolysis, and application of computational fluid dynamics (CFD) to spouted beds. Our original intention was to prepare a sequel to the Mathur and Epstein book, but we soon realized that this chore would be too daunting, especially in view of competing time commitments. We therefore adopted the idea of a multiauthored book for which we would provide editing and prepare a subset of the chapters ourselves. Our intent was to choose an international array of authors able to provide a truly comprehensive view of the field, fundamentals as well as applications. Almost all those whom we asked to participate agreed to do so, and they have been remarkably cooperative in submitting material, following instructions, and responding to requests for changes, many of these being editorial in nature.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.407 | 0.294 |
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".