The role of the arbuscular mycorrhiza-associated rhizobacteria in the biocontrol of soilborne phytopathogens: a review
Bibliographic record
Abstract
The mutualistic symbiosis of most land plants with arbuscular mycorrhizal (AM) fungi has been shown to favor mineral and water nutrition and to increase resistance to abiotic and biotic stresses. This review reports the main mechanisms involved in the control of the disease symptoms and of the intraradical proliferation of soilborne phytopathogens by root colonization with AM fungi, with a special emphasis on the role of the rhizobacteria shown to be specifically associated with the AM extraradical network and the mycorrhizosphere (the soil zone with particular characteristics under the influence of the root/AM association). The mycorrhizosphere would constitute an environment conducive to microorganisms antagonistic to pathogen proliferation. Moreover, attempts to identify rhizobacteria from AM structures and/or the mycorrhizosphere often lead to the isolation of organisms showing strong properties of antagonism on various soilborne pathogens. The ability of AM fungi to control soilborne diseases would be strongly related to their capacity to specifically stimulate the establishment of rhizobacteria unfavorable to pathogen development within the mycorrhizosphere before root infection. Current knowledge concerning the mechanisms involved in AM/rhizobacteria interactions are also described in this review.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".