Effect of pre-harvest treatments on equilibrium moisture contents and safe storage of canola
Bibliographic record
Abstract
For safe storage of canola seeds, the effect of different pre-harvest treatments on seed storability should be determined. Safe storage times of canola seeds (Bayer L233P) with the following five pre-harvest treatments were evaluated: swathed, Glyphosate application + straight cut, Heat and Glyphosate application + straight cut, Reglone application + straight cut, and natural ripening + straight cut. The pre-harvest treated seeds were stored at 20, 25, 30, or 35oC and 52, 63, 75, or 93% relative humidity (RH). The following parameters were measured to estimate the safe storage time: seed equilibrium moisture content (EMC), germination, fatty acid value (FAV), yellow seed count, and invisible mould. The measured EMCs were compared with the EMCs predicted by the equations recommended by the ASABE standard. Different pre-harvest treatments resulted in different desorption properties of canola. None of the ASABE equations was able to predict the measured EMCs correctly. Different pre-harvest treatments had different initial fungal infections. However, these differences did not affect the fungal infection, FAV, germination, and yellow seed counts, with some exceptions for swathed canola in the storage period. The yellow seed count decreased under safe storage conditions (lower than 75% RH and 25oC), but increased at 93% RH and 25oC except for the swathed canola. Therefore, canola seeds with different pre-harvest treatments had a similar storability at below 75% RH or below 30oC. However, the spoilage rates of canola with different pre-harvest treatments at storage conditions of high temperatures (≥30oC) and high RHs (≥75%) were different.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".