Morphological and Molecular Diversity in Turkish Sesame Germplasm and Core Set Selection
Bibliographic record
Abstract
ABSTRACT The conservation of plant germplasm is essential to ensure future breeding gains and crop bio‐diversity. To be truly useful, such germplasm must be characterized for morphological traits and genetic diversity. In this work, agro‐morphological diversity was assessed in 137 Turkish sesame (Sesamum indicum L.) genotypes (129 accessions and eight cultivars) by examination of eight qualitative and nine quantitative traits. As expected, morphological variability in the cultivars was low with broader diversity present in sesame accessions. However, some accessions were identified with interesting features, such as increased number of capsules and seed yield, which could be employed in future cultivar development. The sesame genotypes were analyzed for molecular genetic diversity with 140 amplified fragment length polymorphism (AFLP) loci. The results indicated a relatively low level of variability with an average dissimilarity value of 0.33 for all genotypes. Population structure was also examined and indicated that the material fell into two subpopulations. As most of the accessions (82%) were obtained from the U.S. Department of Agriculture (USDA) and are not yet housed in the Turkish national sesame germplasm collection, the data were used to identify a core set of 22 accessions that should be preserved in Turkey. The importance of using both molecular and morphological data for core selection is highlighted with a focus on germplasm preservation and breeding.
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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 | 0.001 |
| 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) | 0.001 | 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".