Genetic profiling of noncultivated bacteria from the rhizospheres of sugar beet (<i>Beta vulgaris</i>) reveal field and annual variability but no effect of a transgenic herbicide resistance
Bibliographic record
Abstract
In this field study, we compared the bacterial communities inhabiting the rhizosphere of a transgenic, herbicide-resistant sugar beet (Beta vulgaris) cultivar with those of its nonengineered counterpart, using a genetic profiling technique based on PCR amplifications of partial 16S rRNA gene sequences and single-strand conformation polymorphism (SSCP). As a control for the plasticity of the bacterial community, we also analyzed the influence of herbicides, the field heterogeneity, and the annual variation. DNA was isolated from bacterial cell consortia that were directly collected from root material. PCR was carried out with primers that hybridized to evolutionarily conserved regions flanking variable regions 4 and 5 of the 16S rRNA gene. SSCP patterns of these PCR products were composed of approximately 50 distinguishable bands, as detected by silver staining of the gels after electrophoresis. Patterns of the replicates and the different treatments were highly similar, but digital image and similarity analyses revealed differences that corresponded to the positions of the replicates in the field. In addition, communities collected from sugar beet in two successive growing seasons could be distinguished. In contrast, no effect of the transgenic herbicide resistance was detectable. Sequencing of 24 dominant products of the SSCP profiles indicated the presence of bacteria from different phylogenetic groups, with Proteobacteria and members of the Cytophaga-Flavobacterium-Bacteroides group being most abundant.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".