Phytopathological management through bacteriophages: enhancing food security amidst climate change
Bibliographic record
Abstract
The increasing global population and climate change pose significant challenges to agriculture, particularly in managing plant diseases caused by phytopathogens. Traditional methods, including chemical pesticides and antibiotics, have become less effective due to pathogen resistance and environmental concerns. Phage therapy emerges as a promising alternative, offering a sustainable and precise approach to controlling plant bacterial diseases without harming beneficial soil microorganisms. This review explores the potential of bacteriophages as biocontrol agents, highlighting their specificity, rapid multiplication, and minimal environmental impact. We discuss the historical context, current applications, and prospects of phage therapy in agriculture, emphasizing its role in enhancing crop yield and quality. Additionally, the paper examines the integration of phage therapy with modern agricultural practices and the development phage cocktails and genetically engineered phages to combat resistant pathogens. The findings suggest that phage therapy could revolutionize phytopathological management, contributing to global food security and sustainable agricultural practices. ONE-SENTENCE SUMMARY: The burden of plant diseases and phage-based phytopathological treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".