Drying of herbal extract in a draft‐tube spouted bed
Bibliographic record
Abstract
Abstract This work evaluates the feasibility of the draft‐tube spouted bed for drying of herbal extract. Drying runs were carried out according to a central composite design in a conical–cylindrical draft‐tube spouted bed. The variables studied were the percentage of the drying aid (ADJ), the drying gas flow rate relative to gas flow at minimum spouting (Q/Qms), and the flow rate of extract fed to the system relative to the spouting gas flow rate (Ws/Wg). Colloidal silicon dioxide was the drying aid used in order to improve drying performance. Statistical analysis of the effects of processing parameters on product recovery, product accumulation in the bed, and product properties permitted the identification of parameters presenting significant effects on drying. Optimized drying conditions were related to experimental parameters as follow: high levels of the percentage of drying adjuvant (ADJ), high airflow rate relative to minimum spouting (Q/Qms), and low values of the feed flow rate of the extract relative to the gas flow rate (Ws/Wg).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".