Genomic Characterization, Formulation and Efficacy in Planta of a Siphoviridae and Podoviridae Protection Cocktail against the Bacterial Plant Pathogens Pectobacterium spp.
Bibliographic record
Abstract
In the face of global human population increases, there is a need for efficacious integrated pest management strategies to improve agricultural production and increase sustainable food production. To counteract significant food loses in crop production, novel, safe and efficacious measures should be tested against bacterial pathogens. Pectobacteriaceae species are one of the causative agents of the bacterial rot of onions ultimately leading to crop losses due to ineffective control measures against these pathogens. Therefore, the aim of this study was to isolate and characterize bacteriophages which could be formulated in a cocktail and implemented in planta under natural environmental conditions. Transmission electron microscopy (TEM) and genome analysis revealed Siphoviridae and Podoviridae family bacteriophages. To test the protective effect of a formulated phage cocktail against soft rot disease, three years of field trials were performed, using three different methods of treatment application. This is the first study to show the application of a phage cocktail containing Podoviridae and Siphoviridae bacteriophages capable of protecting onions against soft rot in field conditions.
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) | 0.000 | 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 | 0.000 |
| 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".