Variability among Chinese <i>Glycine soja</i> and Chinese and North American Soybean Genotypes
Bibliographic record
Abstract
The narrow genetic base of elite soybean, Glycine max (L.) Merr., germplasm may impede further attempts to improve grain yield and other important agronomic characters. Germplasm collections of wild soybean, Glycine soja Siebold & Zucc., are a source of genetic variability for soybean breeding programs. The objectives of this research were to use genetic markers to characterize diversity among 60 G. soja accessions collected in China and to compare this diversity with 18 U.S. ancestral soybean genotypes, 12 Chinese G. max plant introductions (PIs), and 47 elite soybean lines from the northern USA. These accessions were genotyped with a set of 72 simple sequence repeat markers. The G. soja accessions were found to contain more alleles per locus (17) than the U.S. ancestral genotypes (5.8), the Chinese PIs (5.5), or the elite lines (4.5). Multivariate analyses were able to separate the G. max lines from the G. soja accessions and identify the most diverse subset of G. soja accessions. Multidimensional scaling separated G. soja accessions from high and low latitudes, while Ward's clustering method separated the G. soja accessions into distinct clusters that tended to include accessions from similar geographical regions. These data will be useful to breeders selecting G. soja accessions as parents in a breeding program and for establishing a core collection of G. soja to be used in future research.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".