Physiological and molecular characterization of compost bacteria antagonistic to soil-borne plant pathogens
Bibliographic record
Abstract
Disease suppressive composts have the potential to mitigate the risks associated with chemical pesticides. One of the main characteristics responsible for the suppressive nature of composts is their microbiological populations. To gain insight into the determinants responsible for their suppressive effects, we assayed composts to (i) isolate and identify beneficial antagonistic bacteria, (ii) quantify their antifungal and anti-oomycetal activities, (iii) extract inhibitory compounds produced by the bacteria, and (iv) identify antimicrobial lipopeptides produced by these bacteria. The antagonistic bacteria belonged to the genera Arthrobacter, Pseudomonas, Bacillus, Brevibacillus, Paenibacillus, and Rummeliibacillus and had the ability to antagonise the growth of Fusarium sambucinum, Verticillium dahliae, and (or) Pythium sulcatum. These bacteria produced antimicrobial compounds that affected the mycelial growth and (or) conidial germination of the pathogens. Mass spectrometry analyses showed the presence of various antimicrobial lipopeptides in Bacillus and Bacillus-related spp. extracts, demonstrating that they are responsible, at least in part, for the antagonistic activity of the bacteria. Results from this work provide greater insight into some of the biological, biochemical, and physiological determinants of suppressiveness in composts involved in the control of plant pathogens.
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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 | 0.001 | 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 | 0.000 |
| 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 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".