Comparison of deterioration of rye samples stored at different storage regimes
Bibliographic record
Abstract
8 l5 15 15 17 T7 t9 JJ 42........ 33 vi the major disadvantage in this method is the length of time that is taken.If the harvested grain has very high moisture which can spoil the grain in a few days and if the weather is damp, then heated air drying should be carried out to bring the moisture down to safer levels quickly.In both cases, dried grain has to be cooled to reduce moisture migration and to prevent insect infestation, In Canada, around 80% of the harvested grains are stored on-farm (Muir 2001).This storage time varies from a few weeks to a few years depending upon the use and demand.During such storage periods, maintaining the quality and quantity of the grain is a major concern.Hence safe storage guidelines have to be developed.During storage there are many factors that can affect the quality of grain, such as the temperature at which the grain is stored, moisture content of the gtain, seed maturity and condition, storage time, inter granular gas composition, insects, microorganisms, mites, rodents, birds, dockage, granary structure and geographical location (Jayas 1995).Of all these factors, storage temperature and moisture content of the stored grain are the two main physical factors that have to be monitored continuously.This is because growth and multiplication of all the living organisms in stored grain depend on these two factors (Jayas 1995).When these two factors exceed safe levels, visible and invisible mold starts to grow and a deterioration of grain quality results (Bottomley el al. 1952).Durig storage the quality of grain can be continuously monitored by following a few important parameters such as seed germination, fungal growth, free fatty acid values (FAV) of the grain, gluten quality and nutritive changes (Muir 2001).By closely observing the aforementioned parameters, the deteriolation index of the grain can be determined.
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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) | 0.001 | 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".