An assessment of genetic variation and relationships of smooth bromegrass cultivars and accessions using AFLP markers
Bibliographic record
Abstract
Smooth bromegrass (Bromus inermis Leyss.) is an important cultivated perennial grass species in temperate regions of North America. Information on the genetic diversity and the relationships of available populations is necessary for an effective cultivar improvement program in this crop. The objective of this study was to assess and characterize the genetic variation and relationships of smooth bromegrass cultivars and genebank accessions on the basis of amplified fragment length polymorphism (AFLP) markers. Fourteen wild accessions and 23 cultivars representing several regions of the world were analyzed using six AFLP primer combinations. Of the total number of markers amplified, 90% was polymorphic. A set of 159 robust polymorphic markers was used to genotype the individuals. No AFLP band was specific to an individual bromegrass accession, but bands appeared in different frequencies among the populations. The molecular marker variation within populations of smooth bromegrass (79%) was higher than that among populations (21%). Cultivars developed in the former USSR were the most variable, followed by those developed in the United States of America and Canada. As a group, cultivars were more variable than wild genebank accessions. All of the North American smooth bromegrass cultivars were clustered together, suggesting a common ancestry of North American smooth brome cultivars developed over the past 60 yr. Cultivars developed in the former USSR occurred in several different clusters in the dendrogram, indicating a high among-cultivar diversity. Key words: Bromegrass, accessions, AFLP markers
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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".