Enhancement of population densities of fluorescent pseudomonads in the rhizosphere of tomato plants by addition of acibenzolar-<i>S</i>-methyl
Bibliographic record
Abstract
Fluorescent pseudomonad isolates G309 and CW2, in combination with the resistance inducer acibenzolar-S-methyl (ASM), improved control of fungal and bacterial diseases on tomato plants. The interactions of the bacteria in the presence of ASM showed that in vitro growth of Pseudomonas fluorescens G309 and Pseudomonas sp. strain CW2 was not affected in King's B broth supplemented with 10 and 20 microM ASM. Also, the bacterial cells were not able to utilize ASM as a nutrient source. In vitro production of the two antimicrobial secondary metabolites phenazine-1-carboxylic acid and 2-OH-phenazine by the isolate CW2 was not affected within 3 days from incubation. In contrary, addition of ASM at a concentration of 20 microM to King's B liquid medium significantly increased production of salicylic acid by isolate G309. When roots of tomato plants were treated with G309 or CW2 cell suspensions containing 20 microM ASM, the number of bacterial cells recovered from the rhizosphere was significantly higher in the combined treatments than in the single applications 5, 10, and 15 days after inoculation. However, ASM at a higher concentration (50 microM) did not appreciably enhance the population sizes of either bacterial isolate in the rhizosphere. Enhanced bacterial cell densities in the rhizosphere of tomato plants were also determined following simultaneous treatments of tomato roots with 10 and 20 microM ASM in combination with the transformed isolate G309-384 (mini-Tn5gfp), which encodes the green fluorescent protein.
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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".