Effects of different plant hormones or PEG seed soaking on maize resistance to drought stress
Bibliographic record
Abstract
Yuan, Z., Wang, C., Li, S., Li, X. and Tai, F. 2014. Effects of different plant hormones or PEG seed soaking on maize resistance to drought stress. Can. J. Plant Sci. 94: 1491–1499. Drought stress has a major impact on plant growth and productivity, and seed soaking is an important way to increase seedling resistance to drought stress. This study investigated whether drought hardening chemicals, such as polyethylene glycol (PEG), or plant growth regulators enhance plant drought tolerance. The effects of PEG and several plant hormones, such as indoleacetic acid, gibberellic acid 3, 6-benzylaminopurine (6-BA), on seed germination and seedling growth under drought stress were analyzed. The results revealed that seed soaking with 5×10−3 mg L−1 6-BA or 10% PEG improved maize seed germination parameters under drought stress, including seedlings dry weight, seed vigor and germination rate. In addition, some physiological indices, such as superoxide dismutase and catalase activities, soluble protein contents and malondialdehyde etc. in seedlings under drought stress were improved compared with the control. Therefore, the application of 6-BA or PEG as a seed soak treatment had a significant and synergistic effect on seed germination and seedling growth under drought stress. However, the PEG seed soak treatment maybe slightly lead to plant damage and then improved plant ability to acquire some resistance to stress, the 6-BA were not so.
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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 | 0.001 |
| 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".