Signaling and crosstalk of rhizobacterial and plant hormones that mediate abiotic stress tolerance in plants
Bibliographic record
Abstract
Agricultural areas exhibiting numerous abiotic stressors, such as elevated water stress, temperatures, and salinity, have grown as a result of climate change. As such, abiotic stresses are some of the most pressing issues in contemporary agricultural production. Understanding plant responses to abiotic stressors is important for global food security, climate change adaptation, and improving crop resilience for sustainable agriculture, Over the decades, explorations have been made concerning plant tolerance to these environmental stresses. Plant growth-promoting rhizobacteria (PGPR) and their phytohormones are some of the players involved in developing resistance to abiotic stress in plants. Several studies have investigated the part of phytohormones in the ability of plants to withstand and adapt to non-living environmental factors, but very few have focused on rhizobacterial hormonal signaling and crosstalk that mediate abiotic stress tolerance in plants. The main objective of this review is to evaluate the functions of PGPR phytohormones in plant abiotic stress tolerance and outline the current research on rhizobacterial hormonal communication and crosstalk that govern plant abiotic stress responses. The review also includes the gene networks and regulation under diverse abiotic stressors. The review is important for understanding plant responses to abiotic stresses using PGPR phytohormones and hormonal signaling. It is envisaged that PGPR offer a useful approach to increasing plant tolerance to various abiotic stresses. However, further studies can reveal the unclear patterns of hormonal interactions between plants and rhizobacteria that mediate abiotic stress tolerance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".