Suppression of tomato bacterial speck disease (<i>Pseudomonas syringae</i> pv. <i>tomato</i> (Okabe) Young, Dye, & Wilkie) via induced systemic resistance by <i>Pseudomonas</i> and <i>Bacillus</i> strains
Bibliographic record
Abstract
Bacterial speck Pseudomonas syringae pv. tomato (Pst) (Okabe) Young, Dye, & Wilkie is a widespread disease in tomato plants. Four plant growth-promoting rhizobacterial (PGPR) strains 5(3), 68(2), 36(1), and 47(3) played a significant role (50% and higher) in reducing spot disease severity. Selected strains were identified as Pseudomonas koreensis 5(3), Bacillus mycoides 68(2), Bacillus mojavensis 36(1), and Bacillus simplex 47(3) using the MALDI Biotyper classification system. In planta assay using tomato seedlings were inoculated with the bacterial strains alone or in dual combination. Pseudomonas koreensis 5(3) (51.9%–74.29%) and Bacillus mycoides 68(2) (36.70%–65.56%) both provided a significant reduction in foliar severity caused by bacterial speck disease agent Pseudomonas syringae pv. tomato (Okabe) Young, Dye, & Wilkie. Bacillus simplex 47(3) and Bacillus mojavensis 36(1) were successful only in combined treatments. Defense enzymes Proline, Peroxidase, and Catalase were induced by PGPR strains in comparison with those of control plants. Hydrogen peroxide (H2O2) and callose deposition were evident at reaction sites induced by PGPR strains. The accumulation of callose, H2O2, and high levels of defense enzymes via the treatment of PGPRs might play a significant role in a practical, safe, and effective way to control Pseudomonas syringae pv. tomato.
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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".