Effects of Drought Stress on Germination and Growth Physiology of Mongolian Astragalus
Bibliographic record
Abstract
Objective: This paper used PEG-6000 solution simulated drought stress conditions,explore the response of Mongolian Astragalus seed germination and seedling growth to drought,and provide a technical basis for seedlings of Mongolia Astragalus drought resistant cultivation research. Medthods: We used filter paper for germination bed,clean water for ck,and 10%,20%,30% PEG-6000 treated the seeds. Observe the seed germination and seedling growth even the seedling leaf proline,MDA,soluble sugar,soluble protein and chlorophyll content,to explore the seed germination characteristics and seedling drought resistance. Results:10% PEG-6000 soaked for 48 h can significantly improve the germination rate,20% PEG-6000 soaked for 48 h can significantly improve the seedling growth. MDA and proline content were increased with the increase of PEG-6000 concentration; MDA content were increased with the extension of seed soaking time,there was no significant correlation between seed soaking time with proline content. The root activity,soluble sugar,soluble protein content,chlorophyll a,b,a + b content and a/b values were increased significantly with the extension of seed soaking time; in addition to the chlorophyll b content showed a trend of falling,their content were increased with the increase of PEG-6000 concentration presents a trend of increased before they were reduced.Conclusion: In brief,low concentrations of PEG-6000 is beneficial to seeds germination and seedling growth,but the high concentration can inhibit them. So 20% PEG-6000 soaking 48 h can be choosed using for increasing seedlings drought-resistance.
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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) | 0.000 | 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 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".