Engineering root microbiomes for healthier crops and soils using beneficial, environmentally safe bacteria
Bibliographic record
Abstract
The Green Revolution developed new crop varieties, which greatly improved food security worldwide. However, the growth of these plants relied heavily on chemical fertilizers and pesticides, which have led to an overuse of synthetic fertilizers, insecticides, and herbicides with serious environmental consequences and negative effects on human health. Environmentally friendly plant-growth-promoting methods to replace our current reliance on synthetic chemicals and to develop more sustainable agricultural practices to offset the damage caused by many agrochemicals are proposed herein. The increased use of bioinoculants, which consist of microorganisms that establish synergies with target crops and influence production and yield by enhancing plant growth, controlling disease, and providing critical mineral nutrients, is a potential solution. The microorganisms found in bioinoculants are often bacteria or fungi that reside within either external or internal plant microbiomes. However, before they can be used routinely in agriculture, these microbes must be confirmed as nonpathogenic strains that promote plant growth and survival. In this article, besides describing approaches for discovering plant-growth-promoting bacteria in various environments, including phytomicrobiomes and soils, we also discuss methods to evaluate their safety for the environment and for human health.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".