Water sorption and cooking time of red kidney beans (<i>Phaseolus vulgaris</i> L.): part <scp>II</scp> – mathematical models of water sorption
Bibliographic record
Abstract
Summary Empirical, semi‐theoretical and finite element method (FEM) models were developed to simulate the water sorption of kidney beans. The data of bean moisture content and 1 D swelling ratios obtained in Part I were regressed at different soaking times, and these regression models were used to update the boundary condition and calculate the node coordinates of the FEM model. The developed models were used to calculate the effective water diffusivity (Deff). The developed new empirical model, which considered the soaking temperature and pretreatment history of beans, was the best‐fit equation. The trend of the Deff calculated by the semi‐theoretical model was inconsistent with the water sorption of the beans. The Deff value predicted by the FEM was from 10−3 to 10−7 m2 s−1 and it decreased with the increase in soaking time. There was no significant difference between the moisture contents measured and predicted by the FEM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".