Effects of variable diffusivity on soybean hydration modelling as a Stefan problem
Bibliographic record
Abstract
Abstract The diffusion of moisture in soybean grains is usually followed by two physical facts related to the presence of water: the increase in grain size due to water accumulation and the increase in permeability, as more water is absorbed. In order to increase the physical meaning of the proposed model those two phenomenological facts were inserted in the classical Fick's Second Law of Diffusion. The increase of the grains was taken into account by considering the radius of the grains as a moving boundary of the diffusion system (characterizing a Stefan problem). The increase in permeability was taken into account by considering an exponential dependence of the diffusivity with the moisture content. The behaviour of the main parameters was analyzed with the temperatures. The moisture profiles were calculated for the moving boundary problem as well as the behaviour of the radius of the grains as a function of time. The results showed good agreement with experimental reality of soybean hydration process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".