Multisite Field Evaluation of Bacteriophages for Fire Blight Management: Incorporation of Ultraviolet Radiation Protectants and Impact on the Apple Flower Microbiome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 it