Participation of abscisic acid and gibberellins produced by endophytic<i>Azospirillum</i>in the alleviation of drought effects in maize
Bibliographic record
Abstract
Azospirillum spp. are plant growth promoting bacteria (PGPB) that enhance growth by several mechanisms, including the production of phytohormones such as abscisic acid (ABA), indole-3-acetic acid (IAA), and gibberellins (GAs). Their presence may also alleviate plant water stress. In the present paper, the effects of Azospirillum lipoferum in maize ( Zea mays L.) plants treated with inhibitors of ABA and GA synthesis, fluridone (F) and prohexadione-Ca (P), respectively, and either submitted to drought stress or provided sufficient water, were analysed. Fluridone diminished the growth of plants that had been well watered, in a manner similar to drought, but inoculation with Azospirillum completely reversed this effect. The relative water content of the F-treated and drought-stressed plants was significantly lower (even though drought-stressed plants had been allowed to recover for one week), and this effect was completely neutralized by Azospirillum. These results were correlated with ABA levels assessed by GC-EIMS. Growth was diminished in drought-submitted plants treated with P, alone or combined with F, even though ABA levels were enhanced, suggesting that GAs produced by the bacterium are also important in stress alleviation. The results suggest that both ABA and GAs contribute to water-stress alleviation of plants by Azospirillum.
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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) | 0.001 | 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 | 0.000 |
| 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".