Metabolomics-guided utilization of beneficial microbes for climate-resilient crops
Bibliographic record
Abstract
In the rhizosphere, plants and microbes communicate chemically, especially under environmental stress. Over millions of years, plants and their microbiome have coevolved, sharing various chemicals, including signaling molecules. This mutual exchange impacts bacterial communication and influences plant metabolism. Inter-kingdom signal crosstalk affects bacterial colonization and plant fitness. Beneficial microbes and their metabolomes offer eco-friendly ways to enhance plant resilience and agriculture. Plant metabolites are pivotal in this dynamic interaction between host plants and their interacting beneficial microbes. Understanding these associations is key to engineering a robust microbiome for stress mitigation and improved plant growth. This review explores mechanisms behind plant-microbe interactions, the role of beneficial microbes and metabolomics, and the practical applications for addressing climate change's impact on agriculture. Integrating beneficial microbes' activities and metabolomics' application to study metabolome-driven interaction between host plants and their corresponding beneficial microbes holds promise for enhancing crop resilience and productivity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".