Empirical Modeling of Mean Residence Time in a Co‐Rotating Twin‐Screw Extruder with Rice Flour
Bibliographic record
Abstract
ABSTRACT Mean residence time of rice flour in a twin‐screw extruder was determined using a blue tracer. Variables studied included moisture content, screw speed, barrel temperature, and screw configuration. Mean residence time increased with the increase of the barrel temperature and with the addition of reverse and kneading elements. Mean residence time was significantly related to screw speed, moisture content, die pressure, and screw configuration (P < 0.05). An empirical model was developed to predict mean residence time with the ability to reflect the changes of the barrel temperature and screw configuration. The effects of different extrusion operating conditions including screw speed, moisture content, barrel temperature, and screw geometry on the mean residence time were considered in the model. The validity of the developed model was extensively evaluated and verified using different screw geometries and other processing variables. The mean residence times predicted by the developed model are in good agreement with the experimental data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".