Rhizosphere microbiome: Engineering bacterial competitiveness for enhancing crop production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plants in nature are constantly exposed to a variety of abiotic and biotic stresses which limits their growth and production. Enhancing crop yield and production to feed exponentially growing global population in a sustainable manner by reduced chemical fertilization and agrochemicals will be a big challenge. Recently, the targeted application of beneficial plant microbiome and their cocktails to counteract abiotic and biotic stress is gaining momentum and becomes an exciting frontier of research. Advances in next generation sequencing (NGS) platform, gene editing technologies, metagenomics and bioinformatics approaches allows us to unravel the entangled webs of interactions of holobionts and core microbiomes for efficiently deploying the microbiome to increase crops nutrient acquisition and resistance to abiotic and biotic stress. In this review, we focused on shaping rhizosphere microbiome of susceptible host plant from resistant plant which comprises of specific type of microbial community with multiple potential benefits and targeted CRISPR/Cas9 based strategies for the manipulation of susceptibility genes in crop plants for improving plant health. This review is significant in providing first-hand information to improve fundamental understanding of the process which helps in shaping rhizosphere microbiome.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it