Genetic diversity and relationship of sainfoin (<i>Onobrychis viciifolia</i> Scop.) germplasm as revealed by amplified fragment length polymorphism markers
Bibliographic record
Abstract
Sainfoin (Onobrychis viciifolia Scop.) is a perennial forage legume widely distributed in the northern temperate regions of the world. Its genetic improvement has been relatively slow due, in part, to the lack of genetic information including molecular characterization of sainfoin germplasm. An attempt was made to evaluate genetic diversity and relationships among 38 sainfoin accessions collected from different regions of the world using amplified fragment length polymorphism (AFLP) markers. Five AFLP primer pairs were used to assess 367 individual plants, which produced 1042 polymorphic AFLP bands. The frequencies of the scored bands in all assayed individuals ranged from 0.003 to 0.973, with a mean value of 0.165. The analysis of molecular variance revealed higher within-accession (84.3%) genetic variation than among accessions (15.7%). The genetic distance based on inter-accession distance matrices was significant for most accessions but was not significant for accessions sharing similar parents. A dendrogram of the collected accessions showed two clusters at an inter-accession genetic distance coefficient of 0.36. The revealed information on genetic distance and genetic diversity of the sainfoin accessions is useful for selecting genetically diverse germplasms for sainfoin genetic improvement efforts.
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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".