Genetic Diversity of Canadian Soybean Cultivars and Exotic Germplasm Revealed by Simple Sequence Repeat Markers
Bibliographic record
Abstract
Genetic diversity assessment of improved crop germplasm can facilitate the expansion of the genetic base in a plant breeding program, but little effort has been made to assess the Canadian soybean [Glycine max (L.) Merr.] gene pool established over the past century. Simple sequence repeat (SSR) markers were applied to assess the genetic diversity of 45 Canadian soybean cultivars released from 1934 to 2001 and 37 exotic germplasm accessions. Thirty‐seven SSR primer pairs were applied and 234 polymorphic bands were scored for each accession. The frequencies of the scored bands ranged from 0.01 to 0.90 and averaged 0.17. The proportion of total SSR variation occurring between exotic and Canadian germplasm was 9%; among the Canadian cultivars of three breeding periods 10%; and between the cultivars of maturity groups 0 and 00 4%. More diversity was found for exotic germplasm than the Canadian. More diversity was observed in the cultivars of the recent breeding period than the early. The Canadian cultivars were clustered into seven major groups, partially congruent to the known pedigrees, and they were more related to germplasm from Russia, Sweden, and Ukraine and less to the Asian germplasm. The six genetically most distinct cultivars were PS86 RR, Gaillard, Manitoba Brown, Beechwood, Maple Isle, and 92B91. These findings are useful for the selection of genetically distinct or less related soybean materials to improve the genetic background of the soybean gene pool.
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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 | 0.001 | 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".