Harnessing the plant microbiome to promote the growth of agricultural crops
Bibliographic record
Abstract
The rhizosphere microbiome is composed of diverse microbial organisms, including archaea, viruses, fungi, bacteria as well as eukaryotic microorganisms, which occupy a narrow region of soil directly associated with plant roots. The interactions between these microorganisms and the plant can be commensal, beneficial or pathogenic. These microorganisms can also interact with each other, either competitively or synergistically. Promoting plant growth by harnessing the soil microbiome holds tremendous potential for providing an environmentally friendly solution to the increasing food demands of the world's rapidly growing population, while also helping to alleviate the associated environmental and societal issues of large-scale food production. There recently have been many studies on the disease suppression and plant growth promoting abilities of the rhizosphere microbiome; however, these findings largely have not been translated into the field. Therefore, additional research into the dynamic interactions between crop plants, the rhizosphere microbiome and the environment are necessary to better guide the harnessing of the microbiome to increase crop yield and quality. This review explores the biotic and abiotic interactions that occur within the plant's rhizosphere as well as current agricultural practices, and how these biotic and abiotic factors, as well as human practices, impact the plant microbiome. Additionally, some limitations, safety considerations, and future directions to the study of the plant microbiome are discussed.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".