Variation in the<i>nod</i>gene RFLPs, nucleotide sequences of 16S rRNA genes, Nod factors, and nodulation abilities of<i>Bradyrhizobium</i>strains isolated from Thai<i>Vigna</i>plants
Bibliographic record
Abstract
The analysis of nod genes and 16S rRNA gene regions, Nod factors, and nodulation abilities of Brady rhizobium strains isolated from tropical Thai Vigna species is reported. A total of 55 Bradyrhizobium strains isolated from two cultivated and six wild Vigna species growing in central and northern Thailand were evaluated. Thai Vigna spp. Bradyrhizobium strains showed higher levels of nod gene RFLP diversity compared with Thai soybean Brady rhizobium strains or temperate strains of Bradyrhizobium japonicum and Bradyrhizobium elkanii. Analysis of the 16S rRNA gene region using selected strains also suggests a high genetic diversity of the Thai Vigna-Bradyrhizobium association. Based on thin-layer chromatography analysis, Nod factors produced by tropical Thai Vigna spp. Brady rhizobium strains are more diverse than temperate Japanese and US strains of B. japonicum and B. elkanii. Thai Vigna spp. Bradyrhizobium strains showed variation in nodulation ability and affinity, estimated by the number of normal nodules versus green nodules in an inoculation study. There are some Bradyrhizobium-host combinations that could not form any nodules, suggesting that some genetic differentiation has evolved in their host range. However, most of the Thai Vigna spp. Bradyrhizobium strains formed nodules on the cultigens soybean (Glycine max), mungbean (Vigna radiata), azuki bean (Vigna angularis), and cowpea (Vigna unguiculata). This is the first study on Bradyrhizobium strains associated with a range of cultivated and wild Vigna and reveals that these Bradyrhizobium strains are diverse and may provide novel sources of useful variation for the improvement of symbiotic systems.
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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) | 0.001 | 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".