Biocontrol through antibiosis: exploring the role played by subinhibitory concentrations of antibiotics in soil and their impact on plant pathogens
Bibliographic record
Abstract
There is abundant prior published information on antibiosis, one of the most studied biocontrol mechanisms for plant pathogens. Depending on their concentration, antibiotics can have various effects on target organisms, a phenomenon known as hormesis. Under complex soil conditions where subinhibitory concentrations of antibiotics are thought to prevail, the mechanism of action responsible for disease reduction through antibiosis is often overlooked, where it is generally assumed that biocontrol occurs through mortality of the pathogen. This concept of dose-dependent response must be taken into account to better understand antibiosis and how it can contribute to biocontrol in various ways. This review aims to focus on how antibiotics can operate and persist in soil, act as signalling molecules and enable interactions between soil microbial communities. It also aims to pinpoint specific examples where low, subinhibitory concentrations of antibiotics, which are widespread under natural soil conditions, can reduce disease symptoms by modulating the pathogen’s transcriptome, rather than by toxicity and death. This highlights the need to better understand and characterize as much as possible the mode of action of antibiosis under various complex environmental conditions, in order to anticipate future development of resistance and loss of efficiency through changes in environmental 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".