Mathematical modelling, energy consumption, and quality evaluation of wheat seeds subjected to intermittent drying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This work aims to evaluate the kinetic profile of intermittent drying of wheat seeds using traditional models from the literature and the fractional calculus technique. Furthermore, it aims to verify the application of the intermittent drying process on the amount of antioxidant compounds, protein, and lipid content of the grain, in addition to energy consumption to obtain the desired final moisture content of the wheat. It was verified that the prediction of drying kinetics by Page and fraction order models were similar (modelling efficiency varying between the range of 0.917–0.995, varying the drying condition and wheat cultivar). Regarding antioxidant compounds for the three wheat cultivars, it can be seen that the higher fraction of ethanol (74.0% and 90.36%) used for extraction had greater process efficiency. Regarding the protein content in the three wheat cultivars, lower drying temperatures and intermittency periods result in lower quality losses of material (12.3% for BRS‐Atobá wheat, 9.89% for BRS‐Jacana wheat, and 18.71% for BRS‐Sanhaço wheat). In terms of lipids, it was found that the influence of temperature was greater on the lipid content than on the protein content of the material (for the best drying condition, there were percentage decreases for the best condition of 36.16% for the BRS‐Atobá wheat, 34.9% for BRS‐Jacana, and 52.2% for BRS‐Sanhaço). Regarding energy consumption during the drying process, it can be seen that the conditions used for intermittency and drying time, in addition to the sample conditions, directly impact energy consumption.
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.
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 it