Simple Sequence Repeat Allelic Diversity in Virginia‐Type Peanut Cultivars Released from 1943 to 2006
Bibliographic record
Abstract
Studies on genetic diversity in Arachis spp. using microsatellite markers have included few or no commercial cultivars among the genotypes analyzed. The primary objective of this investigation was to evaluate the utility of simple sequence repeat (SSR) markers for detecting molecular polymorphism among elite virginia‐type peanut germplasm. Within that context, we had a secondary objective of assessing the impact of decades of plant breeding on allelic diversity levels among virginia‐type peanut cultivars. All U.S. virginia‐type cultivated varieties (except four) released between 1943 and 2006 were genotyped at 39 microsatellite loci. A total of 171 alleles were amplified. Allelic frequencies ranged from 0.02 to 0.97, with an average of 0.27. Although no significant difference was observed for the number of alleles present between the initial and the most recent time periods, our results indicate that levels of diversity present in virginia‐type peanuts have fluctuated significantly since the 1940s and peaked during the 1970s. Our study demonstrates that microsatellite markers may be useful for detecting molecular variation among peanut cultivars. Moreover, this is the first report of using microsatellite markers to describe genetic diversity in a collection of cultivated varieties of peanut.
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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".