Rapid identification of <i>Erwinia amylovora</i> and <i>Pseudomonas syringae</i> species and characterization of <i>E. amylovora</i> streptomycin resistance using quantitative PCR assays
Bibliographic record
Abstract
Erwinia amylovora and Pseudomonas syringae are bacterial phytopathogens responsible for considerable yield losses in commercial pome fruit production. The pathogens, if left untreated, can compromise tree health and economically impact entire commercial fruit productions. Historically, the choice of effective control methods has been limited. The use of antibiotics was proposed as an effective control method. The identification of these pathogens and screening for the presence of antibiotic resistance is paramount in the adoption and implementation of disease control methods. Molecular tests have been developed and accepted for identification and characterization of these disease-causing organisms. We improved existing molecular tests by developing methods that are equal or superior in robustness for identifying E. amylovora or P. syringae while being faster to execute. In addition, the real-time PCR-based detection method for E. amylovora provided complementary information on the susceptibility or resistance to streptomycin of individual isolates. Finally, we describe a methodology and results that compare the aggressiveness of the different bacterial isolates on four apple cultivars. We show that bacterial isolates exhibit different behaviors when brought into contact with various apple varieties and that the hierarchical clustering of symptom severity indicates a population structure, suggesting a genetic basis for host cultivar specificity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".