Assessment of genetic diversity in cultivars of white clover (<i>Trifolium repens</i>L.) detected by SSR polymorphisms
Bibliographic record
Abstract
White clover (Trifolium repens L.) is an important temperate pasture legume that plays a key role as a companion to grass species, such as perennial ryegrass (Lolium perenne L.). Due to the outbreeding nature of white clover, cultivars are highly heterogeneous. Genetic diversity was assessed using 16 elite cultivars from Europe, North and South America, Australia, and New Zealand. Fifteen simple sequence repeat markers that detect single, codominant polymorphic genetic loci were selected for the study. The genetic relationships among individuals were compared using phenetic clustering, and those among cultivars were compared using nonmetric multidimensional scaling. Intrapopula tion variability exceeded interpopulation variability, with substantial overlap among populations and weak interpopula tion differentiation. No obvious or significant differentiation was observed on the basis of morphology or geographic origin of the cultivars. The number of parental genotypes used to derive each cultivar was not a major determinant of genome-wide genetic diversity. The outcomes of this assessment of genetic variation in elite white clover germplasm pools have important implications for the feasibility of molecular marker-based cultivar discrimination, and will be used to assist the design of linkage disequilibrium mapping strategies for marker-trait association.
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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) | 0.000 | 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".