Genetic diversity of Bradyrhizobium japonicum within soybean growing regions of the north-eastern Great Plains of North America as determined by REP-PCR and ERIC-PCR profiling
Bibliographic record
Abstract
Abstract While soybean (Glycine max [L.] Merr.) has been grown for several decades in several northern states in the United States, the introduction of early maturing cultivars of soybean in western Canada in the late 1990’s has resulted in a exponential increase in soybean production in the region. Soybean grows in a symbiotic association with Bradyrhizobium japonicum [Kirchner] Jordan, which carries out biological nitrogen fixation within the plant roots. Previous studies have shown that rhizobia introduced from commercial inoculants tend to evolve quickly in soil. In this study, we examined the genetic diversity of 105 B. japonicum isolates from the soybean growing areas of the north-eastern Great Plains of North America by genomic fingerprinting techniques – REP-PCR and ERIC-PCR profiling. High genetic diversity was detected among the B. japonicum isolates sampled across various sites in North Dakota, South Dakota and Minnesota in the United States, and southern Manitoba in Canada. Analysis of the genetic diversity by the unweighted pair group method with an arithmetic mean algorithm (UPGMA) indicated an interesting segregation of isolates between US and Canadian sites. Results of this study also suggest a relatively rapid rate of genetic change within the B. japonicum populations and some evidence that soil texture may influence genetic diversity of the bacterium in the region.
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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 | 0.001 |
| 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".