Development of a novel biological control agent targeting the phytopathogen Erwinia amylovora
Bibliographic record
Abstract
Antibiotics are used extensively to control animal, plant, and human pathogens. They are sprayed on apple and pear orchards to control the bacterium Erwinia amylovora , the causative agent of fire blight. This phytopathogen is developing antibiotic resistance and alternatives either have less efficacy, are phytotoxic, or more management intensive. The objective of our study was to develop an effective biological control agent colonizing the host plant and competing with Erwinia amylovora. It must not be phytotoxic, have a wide spectrum of activity, and be unlikely to induce resistance in the pathogen. To this end, several bacterial isolates from various environmental samples were screened to identify suitable candidates that are antagonistic to E. amylovora . We sampled bacteria from the flowers, leaves, and soil from apple and pear orchards from the springtime bloom period until the summer. The most effective bacteria, including isolates of Pseudomonas poae , Paenibacillus polymyxa , Bacillus amyloliquefaciens and Pantoea agglomerans , were tested in vitro and in vivo and formulated into products containing both the live strains and their metabolites that were stable for at least 9 months. Trees treated with the product based on P. agglomerans NY60 had significantly less fire blight than the untreated control and were statistically not different from streptomycin-treated control trees. With P. agglomerans NY60, fire blight never extended beyond the central vein of the inoculated leaf. The fire blight median disease severity score, 10 days after inoculation, was up to 70% less severe on trees treated with P. agglomerans NY60 as compared to untreated controls.
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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 | 0.000 | 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) | 0.001 | 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".