Root Microbial Functions and Robust Network Drive High-Yielding Canola Genotype
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 and their root microbiomes have coevolved complex relationships that influence growth and development. Although plant genotype is known to shape root microbiomes, a detailed understanding of this interplay remains limited. We used shotgun metagenomic sequencing to examine the composition, function, and microbial association networks of root bacterial communities in two Brassica napus (canola) genotypes with contrasting yields: NAM-23 and NAM-30. Root samples were collected at three developmental stages (vegetative, flowering, and maturation) across three field sites. Growth stage significantly influenced both alpha and beta diversity, but not Kyoto Encyclopedia of Genes and Genomes pathway functions. Genotype had minimal effect on overall diversity and function but specifically influenced the recruitment of certain bacterial taxa and the topology of microbial association networks. NAM-23, the high-yielding genotype, was enriched in plant-growth-promoting bacteria ( Rahnella) and genes related to carbohydrate and phosphorus metabolism. Additionally, NAM-23 exhibited a more robust and connected microbial network, with higher degree, betweenness, and clustering coefficient, and more genotype-specific hub taxa, traits often associated with enhanced resistance to abiotic and biotic stresses and potentially contributing to its higher yield. Our findings provide a high-resolution view of genotype-specific interactions with the root microbiome, highlighting key microbial features associated with high yield. These insights support microbiome-informed strategies for crop improvement in sustainable agriculture. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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