Postharvest control of blue mold and gray mold on apples using isolates of<i>Pseudomonas syringae</i>
Bibliographic record
Abstract
Four isolates of Pseudomonas syringae, MA-4, MB-4, MD-3b, and NSA-6, from the phyllospere of apple trees, were evaluated as biocontrol agents for controlling blue mold [Penicillium expansum] and gray mold [Botrytis cinerea] of apples (Malus domestica) in storage. When separately co-inoculated with the two pathogens, the four isolates at a concentration of 1 × 107 CFU/mL controlled blue mold by 78–94% after incubation at 20°C for 5 days and by 64–70% at 4°C for 28 days. The incidence of gray mold was also significantly reduced by P. syringae isolates MD-3b, NSA-6, and MA-4 under these conditions. However, a higher concentration of P. syringae MA-4 and NSA-6 was required to control gray mold compared to blue mold. Spray treatment with P. syringae MA-4 significantly controlled blue mold on 'Empire' and 'Delicious' apples at 18 and 4°C. Under conditions similar to those of controlled atmosphere in commercial storage, a dip treatment with P. syringae MA-4 significantly controlled blue mold on 'Empire' and 'Delicious' apples and was more effective than the treatment with BioSave™, a commercially available biofungicide. Pseudomonas syringae MA-4 at a concentration of 1 × 108 CFU/mL controlled blue mold as efficiently as the chemical treatment with a combination of thiabendazole and diphenylamine.Key words: biocontrol, postharvest, Malus domestica, Penicillium expansum, Botrytis cinerea.
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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) | 0.001 | 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".