Soil and Rhizosphere Bacterial Diversity in Maize Agro- Ecosystem
Bibliographic record
Abstract
Management practices used in maize production have an impact on soil agro- ecosystems where different microbial communities coexist. Soil inhabiting bacteria are numerous and diverse, but we know very little about their ecological distribution. Here we analyzed the bacterial community diversity in the rhizosphere of two transgenic maize cultivars, in agricultural soil before sowing and in non-cultivated soil in an experimental site in the south region of Uruguay. We followed two culture-independent methods: DGGE (denaturing gradient gel electrophoresis) and 454-pyrosequencing of 16S rRNA gene amplicon. Through pyrosequencing, the three environments analyzed presented differences in terms of bacterial composition. However, no differences were found in the relative abundance of the ten most represented phyla in the rhizosphere of the two cultivars at different phenological stages. We found significant differences of Bacteroidetes, Gemmatimonadetes, Planctomycetes, Proteobacteria and Verrucomicrobia phyla when comparing agricultural and non-cultivated soils, as well as a significant enrichment of members of the phylum Gemmatimonadetes in all rhizosphere samples compared to soil. Through DGGE analysis we evidenced that maize rhizosphere bacterial communities changed at different phenological stages in both cultivars. We also provided baseline information about bacterial specific taxa within maize agro- ecosystem for further evaluation of possible rhizosphere bacterial community shifts of genetically modified maize cultivars under different management practices.
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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 | 0.001 | 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".