Effect of Harvesting Time and Drying Temperature on Soybean Seed Quality
Bibliographic record
Abstract
In order to evaluate the effect of seed moisture content at harvesting time and drying temperature on soybean seed germination characteristics, an experiment was conducted as factorial (2×3×2) based on Randomized Complete Block Design with three replications in Karaj and Moghan areas. The first factor was cultivar with two levels (Williams and L17), the second factor was seed moisture content with three levels (15, 20 and 25%) and the third factor was drying temperature with two levels (30 and 45 °C). Results show that the germination percentage of producing seeds in Karaj (81.3%) were higher than Moghan area (77.4%). The difference between the germination percentage of dried seeds at 30 and 45◦C with initial seed moisture content of 15% was about 5.5%, while this difference for seeds with 25% initial moisture content was about 18.5%. There was no significant difference between seedling vigor index of seeds with 15% moisture content of two cultivars that have been produced in Karaj, but at 20% moisture content, the seedling vigor index in Williams cultivar (139.7) was higher than L17 cultivar (107.3). The highest (51.42 µsm cm-1 g-1) electrical conductivity of seed leakage soluble was obtained from the L17 cultivar with 25% moisture content and 45◦C and the lowest (28.46 42 µsm cm-1 g-1) rate of it was observed in Williams cultivar with 20% moisture content and 30◦C. According to these results, we must harvest and dry soybean seeds at 30◦C when its moisture content reached under 20%.
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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 | 0.000 |
| 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) | 0.001 | 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".