Effects of Priming Treatment with PEG on the Seed Germination and Seedling Growth of Non-heading Chinese Cabbage
Bibliographic record
Abstract
The seeds of three non-heading Chinese cabbage cultivars Xinxiaqing 2,Xiadong qing,and Huawang were used as experimental materials,and the effects of priming treatment with PEG on the seed germination and physiological characters of seedlings were studied under different temperatures.The results showed that after priming treatment germination rate,germination vigor,germination index,vigor index of seeds,and the fresh and dry weights of seedlings were higher than those of control.The priming treatment with PEG significantly improved the activities of superoxide dismutase(SOD),peroxidase(POD) and catalase(CAT) of the seedlings but decreased the content of malondiadehyde(MDA),resulting in that the soluble protein and soluble sugar contents in the treated seedlings were significantly higher than those of control.There were some differences in priming effects among different cultivars.The results showed that priming treatment with PEG could improve both the seed vigor and stress resistance of non-heading Chinese cabbage seedlings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".