The Optimum Moisture Content Range of Ultra-dry Storage for Different Type of Seeds
Bibliographic record
Abstract
Wheat,soybean and welsh onion seeds were used as materials to detect the compatibility and identify the optimum moisture content range under ultra-dry storage condition.Seeds were dried to lower than 5% moisture content for ultra-dry storage.The germination tests were conducted to analyze the ultra-dry storage compatibility.The various moisture contents seeds were stored at room temperatures and high temperatures for different time.Through detecting germination apability and vigour to identify the optimum moisture contents.The results showed that seeds with lower than 5% moisture content had much higher vigour than that with ordinary moisture contents,and moderate ultr-dry seeds with 3.0% to 4.0% moisture content had stronger vigor than that of bone-dry seeds with 2.3%.At room temperature,the optimum moisture content of wheat seeds for ultra-dry storgage was 2.6% to 7.0%,soybean was 4.2% to 5.1%,and onion seeds was 2.2% to 3.5%.When temperature was higher than 45 ℃,the optimum moisture content of wheat seeds was 2.8% to 5.0%,soybean was 3.5% to 4.2%,and onion seeds was 1.4% to 5.0%.The optimum moisture content was different from type of seeds and storage temperature for ultra-dry storage.The optimum moisture content of oily seeds was lower than that of protein seeds and starch seeds.
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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".