Development of High-Throughput Molecular Markers for Soybean Breeding
Bibliographic record
Abstract
The microbial community structure in the rice rhizosphere plays a crucial role in plant health and soil nutrient cycling throughout the growing season. This study investigates how rice plants ( Oryza sativa ) influence the microbial community in rice field soil over different growth stages. Using quantitative PCR and 16S rRNA gene pyrotag analysis, we compared the microbial communities in the rhizosphere of rice plants to those in unplanted bulk soil. Our findings indicate that the rhizosphere harbors a significantly higher abundance of 16S rRNA genes, suggesting enhanced microbial growth. The rhizosphere effect was more pronounced than temporal changes, with notable shifts in the presence of specific microbial phyla such as Gemmatimonadetes , Proteobacteria , and Verrucomicrobia . Functional groups like potential iron reducers and fermenters were enriched in the rhizosphere. Additionally, a Herbaspirillum species was consistently more abundant in the rhizosphere, particularly during the early growth stages. These results underscore the dynamic interactions between rice plants and their associated microbial communities, highlighting the importance of the rhizosphere in shaping microbial diversity and function over the growing season.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".