Biological Control of Bacterial Speck of Tomato Under Field Conditions at Several Locations in North America
Bibliographic record
Abstract
ABSTRACT Bacterial speck of tomato, caused by Pseudomonas syringae pv. tomato, continues to be a problem for tomato growers worldwide. A collection of nonpathogenic bacteria from tomato leaves plus P. syringae strains TLP2 and Cit7, P. fluorescens strain A506, and P. syringae pv. tomato DC3000 hrp mutants were examined in a greenhouse bioassay for the ability to reduce foliar bacterial speck disease severity. While several of these strains significantly reduced disease severity, P. syringae Cit7 was the most effective, providing a mean level of disease reduction of 78% under greenhouse conditions. The P. syringae pv. tomato DC3000 hrpA, hrpH, and hrpS mutants also significantly reduced speck severity under greenhouse conditions. The strains with the greatest efficacy under greenhouse conditions were tested for the ability to reduce bacterial speck under field conditions at locations in Alabama, Florida, and Ontario, Canada. P. syringae Cit7 was the most effective strain, providing a mean level of disease reduction of 28% over 10 different field experiments. P. fluorescens A506, which is commercially available as Blight-Ban A506, provided a mean level of disease reduction of 18% over nine different field experiments. While neither P. syringae Cit7 nor P. fluorescens A506 can be integrated with copper bactericides due to their copper sensitivity, there exist some potential for integrating these biological control agents with "plant activators", including Actigard. Of the P. syringae pv. tomato DC3000 hrp mutants tested, only the hrpS mutant reduced speck severity significantly under field conditions.
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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 | 0.001 | 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".