Recent Developments in the Study of Plant Microbiomes
Bibliographic record
Abstract
To date, an understanding of how plant growth-promoting bacteria facilitate plant growth has been primarily based on studies of individual bacteria interacting with plants under different conditions. More recently, it has become clear that specific soil microorganisms interact with one another in consortia with the collective being responsible for the positive effects on plant growth. Different plants attract different cross-sections of the bacteria and fungi in the soil, initially based on the composition of the unique root exudates from each plant. Thus, plants mostly attract those microorganisms that are beneficial to plants and exclude those that are potentially pathogenic. Beneficial bacterial consortia not only help to promote plant growth, these consortia also protect plants from a wide range of direct and indirect environmental stresses. Moreover, it is currently possible to engineer plant seeds to contain desired bacterial strains and thereby benefit the next generation of plants. In this way, it may no longer be necessary to deliver beneficial microbiota to each individual growing plant. As we develop a better understanding of beneficial bacterial microbiomes, it may become possible to develop synthetic microbiomes where compatible bacteria work together to facilitate plant growth under a wide range of natural conditions.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".