ANN-Based Models for Moisture Diffusivity Coefficient and Moisture Loss at Equilibrium in Osmotic Dehydration Process
Why this work is in the frame
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Bibliographic record
Abstract
Equations were developed using artificial neural networks to predict water diffusivity coefficient (D e ) and moisture loss at equilibrium point (ML ∞) in order to get the moisture loss (ML) at any time in osmotic dehydration of fruits. These models mathematically correlate nine processing variables (temperature and concentration of osmotic solution, water and solid composition of the fruit, porosity, surface area, characteristic length, solution-to-fruit mass ratio, and agitation level) with D e and ML ∞. Models were developed using a wide variety of data from the literature and they were able to predict D e (r = 0.98) and ML ∞(r = 0.94) in a wide range of variable conditions. With these two parameters known, ML can be calculated using Crank's solutions of Fick's second law. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet, using a set of equations, makes them very useful and practical for process design and control.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it