Plant Growth Stimulation by Microbial Consortia
Bibliographic record
Abstract
Plant-associated microorganisms play an important role in agricultural production. Although various studies have shown that single microorganisms can exert beneficial effects on plants, it is increasingly evident that when a microbial consortium—two or more interacting microorganisms—is involved, additive or synergistic results can be expected. This occurs, in part, due to the fact that multiple species can perform a variety of tasks in an ecosystem like the rhizosphere. Therefore, the beneficial mechanisms of plant growth stimulation (i.e., enhanced nutrient availability, phytohormone modulation, biocontrol, biotic and abiotic stress tolerance) exerted by different microbial players within the rhizosphere, such as plant-growth-promoting bacteria (PGPB) and fungi (such as Trichoderma and Mycorrhizae), are reviewed. In addition, their interaction and beneficial activity are highlighted when they act as part of a consortium, mainly as mixtures of different species of PGPB, PGPB–Mycorrhizae, and PGPB–Trichoderma, under normal and diverse stress conditions. Finally, we propose the expansion of the use of different microbial consortia, as well as an increase in research on different mixtures of microorganisms that facilitate the best and most consistent results in the field.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".