Multisite Field Evaluation of Bacteriophages for Fire Blight Management: Incorporation of Ultraviolet Radiation Protectants and Impact on the Apple Flower Microbiome
Bibliographic record
Abstract
Fire blight, a disease of pome fruits caused by the bacterium Erwinia amylovora, has become increasingly difficult to manage after the emergence of streptomycin-resistant strains. Alternative antibiotics and copper are available; however, these chemicals have use restrictions in some countries and also can carry risks of phytotoxicity. Therefore, there is growing interest in biological-based management options, with bacteriophage (phages) showing promise, as these naturally occurring pathogens of bacteria are easy to isolate and grow. However, there are several technical challenges regarding the implementation of phage biocontrol in the field, as the viral molecules suffer from ultraviolet radiation (UVR) degradation and can die off rapidly in the absence of the host bacterium. In this work, we assessed the efficacy of Erwinia phages and a commercial phage product for blossom blight control in the field across multiple locations in the eastern United States. In these tests, disease control ranged from 0.0 to 82.7%, and addition of a UVR protectant only resulted in significantly increased disease control in 2 of 12 tests. We also analyzed microbial community population changes in response to phage application. Changes in bacterial community diversity metrics over time were not detected; however, relative abundances of target taxa were temporarily reduced after phage applications, indicating that these phage applications did not have deleterious effects on the flower microbiome. We have demonstrated that biological control of fire blight with phages is achievable, but a better understanding of phage−pathogen dynamics is required to optimize disease control efficacy.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".