Effect of PEG Treatment on Fresh and Aged Seed Germination and Seedling Growth of Andrographis paniculata( Burm.f.) Nees
Bibliographic record
Abstract
To study the capability of seed germination under osmotic stress of A. paniculata fresh seeds and drought resistance of their seedlings,and to explore the measurement of improving the vigor of aged seeds, A. paniculata fresh seeds were treated with different water potential of PEG-6000,and aged seeds of A. paniculata were soaked in PEG-6000 to explore the optimal priming measurement,then the germination starting time, germination rate,germination vigor of A. paniculata seeds,and radicle length,hypocotyl length,fresh weight of A. paniculata seedlings were determined. The results showed that: There was certain drought resistance in the process of seed germination and seedling growth of A. paniculata. The average germination percentage and radicle length of A. paniculata had no significant difference compared with the control when treated with mild drought stress,but moderate and highly drought stress could inhibit seed germination and seedling growth significantly. PEG priming could promote seed germination and seedling growth of A. paniculata,but the effect of the treatment was closely related to the water potential of PEG-6000 and aging degree of seeds. Seed germination percentage was only 8. 5% after dry storage for 24 months at room temperature,it was increase to 20. 5% after- 0. 8 MPa PEG priming.
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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".