ROS and Oxidative Response Systems in Plants Under Biotic and Abiotic Stresses: Revisiting the Crucial Role of Phosphite Triggered Plants Defense Response
Bibliographic record
Abstract
species. Phi can also control disease symptoms and the spread of pathogenic bacteria, fungi, and nematodes. Phi plays critical roles as a fungicide, pesticide, fertilizer, or biostimulator. Overall, Phi can alleviate the severity of the disease caused by oomycete, fungi, pathogenic bacteria, and nematodes (leave, stem, fruit, tuber, and root) in various plants (vegetables, fruits, crops, root/tuber crops, ornamental plants, and forests). Advance research in molecular, physiological, and biochemical approaches has approved the key role of Phi in enhancing crop growth, quantity, and quality of several plant species. Phi is chemically similar to orthophosphate, and inside the cells, it is likely to get involved in different features of phosphate metabolism in both plants and pathogens. In plants, a range of physiobiochemical alterations are induced by plant pathogen stress, which causes lowered photosynthesis activities, enzymatic activities, increased accumulation of reactive oxygen species (ROS), and modification in a large group of genes. To date, several attempts have been made to study plant-pathogen interactions with the intent to minimize the loss of crop productivity. Phi's emerging function as a biostimulant in plants has boost plant yield and tolerance against various stress factors. This review discusses Phi-mediated biostimulant effects against biotic and abiotic stresses.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".