Heat and mass transfer modelling for storage of food bulk raw materials under active ventilation
Bibliographic record
Abstract
Abstract The paper aims to research a possibility of storing plant agricultural raw materials. Rational management of natural resources is a dominant trend in the economy development, which involves the fullest processing of plant raw materials intended for human consumption with minimum waste. Active grain ventilation is conducted by drying at a low temperature to preserve the quality of raw and wet grain, as well as by cooling grain batches stored to increase their durability. With a certain moisture content grain can be gradually dried, cooled, preserved, aerated depending on its condition and purpose. These technological methods provide a significant reduction in energy costs in comparison with thermal drying, as well as improving the quality of seeds and grains due to the “soft” completion of the biochemical processes associated with maturation and stabilization of the protein-enzyme complex. Active ventilation requires no complex equipment or large capital investments. Therefore, it is no coincidence that the technologies developed on the basis of active ventilation are widely used in processing of main volumes of high quality grain in USA, Canada, and Australia. The intensification of the grain preparation process achieved by this way reduces energy costs of drying. The study models the storage conditions for bulks of various types of agricultural raw materials under active ventilation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".