Development of a novel biological control agent targeting the phytopathogen Erwinia amylovora
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
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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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 | 0.000 | 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 it