Culturable bacterial microflora associated with nectarine fruit and their potential for control of brown rot
Bibliographic record
Abstract
Microflora of fruit surfaces have been the best source of antagonists against fungi causing postharvest decay of fruit. However, there is little information on microflora colonizing surfaces of fruits other than grape, apple, and citrus. We characterized bacterial microflora on nectarine fruit surfaces from the early stage of development until harvest. Identification of bacterial strains was made using MIDI (fatty acid methyl ester analysis) and Biolog systems. Biolog identified 35% and MIDI 53% of the strains. Thus results from MIDI were used to determine the frequency of occurrence of genera and species. The most frequently occurring genera were Curtobacterium (21.31%), followed by Pseudomonas (19.99%), Microbacterium (13.57%), Clavibacter (9.69%), Pantoea (6.59%), and Enterobacter (4.26%). The frequency of isolations of some bacteria - for example, the major pseudomonads (Pseudomonas syringae, Pseudomonas putida, and Pseudomonas savastanoi) or Pantoea agglomerans - tended to decline as fruit developed. As Pseudomonas declined, Curtobacterium became more dominant. Time of isolation was a significant factor in the frequency of occurrence of different bacteria, indicating succession of the genera. Throughput screening of the bacterial strains against Monilinia fructicola on nectarine fruit resulted in the detection of strains able to control brown rot. The 10 best-performing antagonistic strains were subjected to secondary screening. Four strains reduced decay severity by more than 50% (51.7%-91.4% reduction) at the high pathogen inoculum concentration of 105 conidia/mL.
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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) | 0.001 | 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".