Influences of Tow Chemical Treatments on the Germination Ability and the Vigor of Aging Mulberry Seeds
Bibliographic record
Abstract
In order to explore the methods of improving the vigors of aging seeds,the mulberry seeds naturally aged were soaked with different concentrations of Gibberellin(GA3) and ascorbic acid(ASA) for 24 hours.The seed germination vitalities,the α-diastatic activity and the contents of soluble protein,the soluble sugar and the malondialdehyde(MDA) in the leaves of seedlings were detected.The results showed that the two chemical reagent had some role in promoting mulberry seed germination ability,and their effects showed the relationship with the applied concentrations.The best effects were the treatments of 500 mg/L ASA and 50 mg/L GA3,compared with the control,the germination rate,the germination index,the α-diastatic activity,contents of soluble protein and soluble sugar were respectively increased by 13.87%,16.18%,55.68%,17.20% and 30.53%,while under the treatments of 50 mg/L GA3,respectively increased by 4.63%,14.72%,28.41%,12.45% and 12.98%.The MDA contents reduced 56.32% with treatment of 500 mg/L ASA,and 37.62% with treatment of 50 mg/L.In addition,the results also showed that between the treatments of 500 mg/L ASA,in addition to α-diastatic activity,the other physiological indexes had significant or highly significant differences,while between the treatments of 20 mg/L GA3,except germination ratio and soluble protein content,the other physiological indexes also had significant or highly significant differences.Based on a comprehensive analysis of the effects on the germination ability and the vigor of aging mulberry seeds,the effects of 500 mg/L ASA was better than the effects of 50 mg/L GA3.
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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".