Consequences of Seed Priming with Salicylic Acid and Hydro Priming on Smooth Vetch Seedling Growth under Water Deficiency
Bibliographic record
Abstract
Due to low rainfall at early autumn, smooth vetch seedling growth in rain-fed lands often is limited by water deficit stress yet the data regarding the reactions of smooth vetch to water deficit at early growth stages are pretty rare. The objective of current study was to examine possibility of using priming treatments (hydro priming and priming salicylic acid) to alleviate the inhibitory effect of water deficiency during early growth of Smooth Vetch. In this respect, seeds were soaked in distilled water (hydro priming) or 0.5 mM solution of SA for 36 h at 10 °C then dried back to original moisture content. Pots were irrigated for 25 days at four levels of available water containing field capacity (FC), 75% FC, 50% FC and 25% FC. In general, seedling emergence and early growth were markedly limited by increasing water deficiency. However, priming treatments particularly with SA caused considerable improvement in either emergence or growth of seedlings (dry weight, length). The obtained results showed that primed samples exhibited higher accumulation of proline, glycine betaine (GB) under all levels of available water except 100% FC and also higher total soluble sugars (TSS) and trehalose under severe water deficit (25% FC). SA primed samples had higher relative water content especially under higher levels of water deficiency. The more balanced water status within SA primed samples also was accompanied with higher accumulation of proline and glycine betaine. There were significant differences between two priming treatments in terms of proline and GB content within seedlings and SA priming considerably increased proline and GB accumulation. In contrast to proline and GB, TSS and trehalose content wasn’t influenced by SA treatment and both hydro and SA primed samples showed statistically similar quantities.
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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".