Potential mode of action of multispecies inoculums on wheat growth under water stress
Bibliographic record
Abstract
Abstract Manipulating microbial communities could increase crop resistance to environmental stressors such as drought. It is, however, not clear what would be the best approach to do so and what microbial traits are important. Here, we first compare multispecies inoculums created using different approaches. The only inoculum that increased wheat fresh biomass under drought was the one created from 25 isolates that had showed a capacity to grow under high osmolarity. We then looked at two potential mechanisms of action of this inoculum: (i) direct action, by sequencing and screening the genomes of the inoculated bacteria, (ii) indirect action, by sequencing the 16S ribosomal ribonucleic acid gene and internal transcribed spacer region of rhizosphere, root, and leaves microbial communities. The microbes in the inoculum harbored many traits related to plant growth promotion, competition, and water stress resistance. The inoculation also resulted in significant shifts in the microbial communities associated with wheat, including some microorganisms (e.g. Rhizobium, Shinella, and Klebsiella) previously reported to improve plant drought resistance. We conclude that the inoculum studied here increased wheat growth because it potentially acted on two fronts: directly, through the traits it was selected for, and indirectly, through inducing shifts in the resident plant microbial communities.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".