Molecular Identification and Characterization of Seeded Turf Bermudagrass Cultivars Using Simple Sequence Repeat Markers
Bibliographic record
Abstract
Core Ideas Six to nine signature alleles were revealed for each of the four standard seeded bermudagrass cultivars with 32 SSR markers. All individual bermudagrass plants were correctly assigned to their source cultivars using the SSR markers. The SSR marker profiles can be effectively used to identify blinded individuals to the source cultivars. Common bermudagrass [Cynodon dactylon (L.) Pers. var. dactylon], is increasingly used in the development of seed‐propagated turf cultivars. Each seeded bermudagrass cultivar is a heterogeneous population composed of heterozygous genotypes. Therefore, the accurate identification of seeded bermudagrass cultivars is a challenge and has not yet been reported although this kind of information would be valuable for new cultivar development, seed certification, and intellectual property protection. Accordingly, the objectives of this study were (i) to characterize the genetic diversity within and relatedness between turf‐type seeded cultivars; and (ii) to investigate the assignment of individuals to their source cultivars using simple sequence repeat (SSR) markers. Four seeded cultivars, NuMex Sahara, Princess‐77, Riviera, and Yukon were used in the investigation. Thirty individual plants, two bulk samples, and two additional individual plants from each of the four cultivars were genotyped with 32 SSR markers that were sampled to span a major portion of the species genome. The number of alleles amplified per SSR locus ranged from 3 to 10, with a mean of 5. Six to nine signature alleles were identified for each of the four cultivars. Genetic distance estimates and clustering results were consistent with the respective breeding history. Individual plants formed four distinct groupings corresponding exactly to the four cultivars and all individuals were correctly assigned to their respective source cultivars. The total gene diversity of the four cultivars was 0.257, indicating high diversity. The posterior test also indicated that bulk samples and two additional individual plants were clearly assigned to their source cultivars. The approach developed in this study is useful for the accurate identification of seeded bermudagrass cultivars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".