Strategy to select and assess antagonistic bacteria for biological control of<i>Rhizoctonia solani</i>Kühn
Bibliographic record
Abstract
A screening strategy was developed to assess the potential of plant-associated bacteria to control diseases caused by Rhizoctonia solani Kühn. About 434 already characterized antagonistic bacterial strains isolated from diverse plant species and microenvironments were evaluated for biocontrol and plant growth promotion by a hierarchical combination of assays. Analyzing in vitro antagonism towards different Rhizoctonia isolates resulted in a selection of 20 potential biocontrol agents. The strains were characterized by their antagonistic mechanisms in vitro as well as their production of the plant growth hormone indole-3-acetic acid. The plant growth promoting effect by antagonistic bacteria was determined using a microtiter plate assay on the basis of lettuce seedlings. Lettuce and sugar beet as host plant were included in the biocontrol experiments in which the antagonistic effect of 17 bacterial isolates could be confirmed in vivo. Sequencing of the 16S rDNA gene and (or) fatty acid methyl ester gas chromatography was used to identify the antagonistic isolates. Molecular fingerprints of isolates obtained by BOX-polymerase chain reaction were compared to avoid further investigation with genetically very similar strains and to obtain unique molecular fingerprints for quality control and patent licensing. According to our strategy, an assessment scheme was developed and four interesting biological control agents, Pseudomonas reactans B3, Pseudomonas fluorescens B1, Serratia plymuthica B4, and Serratia odorifera B6, were found. While S. plymuthica B4 was the best candidate to biologically control Rhizoctonia in lettuce, P. reactans B3 was the best candidate to suppress the pathogen in sugar beet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".