Development of High-Throughput Molecular Markers for Soybean Breeding
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
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.
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 | 0.000 | 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 it