Analysis of diversity and relationships among orchardgrass (Dactylis glomerata L.) accessions using start codon-targeted markers
Bibliographic record
Abstract
Orchardgrass, or cocksfoot, is an important perennial forage grass worldwide. The comprehensive understanding of orchardgrass accessions will benefit germplasm collection and breeding progress, and it will enhance efforts to improve forage yield and quality. Therefore, 24 novel, simple, polymorphic, and reliable start codon-targeted (SCoT) markers were used to analyze the diversity and genetic relationships among 95 orchardgrass accessions. In total, 273 polymorphic bands were detected with an average of 11.4 bands per primer. The average polymorphic rate for the species was 83.4%, suggesting a high discriminating ability of the SCoT technique for orchardgrass. The molecular variance analysis revealed that 69.13 and 30.87% of variation resided within and among groups, respectively, demonstrating that the orchardgrass germplasms had a higher level of genetic diversity within groups than among geographical regions and distributions. The distinct geographical divergence of orchardgrass was revealed between North America and Oceania. The unweighted pair-group method with arithmetic mean dendrogram revealed a separation of 7 main clusters between 95 accessions according to the geographical origin. Furthermore, each cluster was divided into subgroups mainly according to the origin of its state. The genetic divergence of orchardgrass might be influenced by the ecogeographical conditions, climatic types, breeding systems and gene flow with variations in cultures, bird migration, and breeder selection. These results could facilitate orchardgrass germplasm collection, management, and breeding worldwide.
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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".