Transferability of Sorghum Microsatellite Markers to Bamboo and Detection of Polymorphic Markers
Bibliographic record
Abstract
The use of molecular markers for the characterization and evaluation of plant genetic resources has become a useful approach in plant genetic research. Simple Sequence Repeats (SSRs) are among the markers that are widely used in genetic diversity and parental analysis owing to their co-dominant nature, high reproducibility, abundance in the genome and transferability across species or genera. The development of these markers for a species might be costly and time consuming. Hence, screening existing markers through transferability test from closely related species or family is resource conscious. In this study, the transferability of 90 polymorphic SSR markers of sorghum to bamboo was tested and polymorphic analysis of transferable markers were performed. Nearly 62% of the tested SSRs successfully recorded amplification in at least one bamboo species of which 55% were polymorphic. These polymorphic markers detected a total of 147 alleles at an average rate of 4.7 alleles per marker. The abundant alleles account 20.4% while the common and rare alleles share 39.6 and 40 %, respectively. The result showed a relatively low degree of polymorphic information content (PIC) averaging 0.29. The gene diversity index (He) ranged from 0.21 to 0.49 with a mean of 0.37. The cluster analysis based on the polymorphic markers surfaced most of the species in accordance with their geographic origin. The complementarity of the weighted neighbour joining tree and coordinate analysis implies the representative nature of the transferred markers for the diversity analysis of bamboo species.
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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 | 0.001 | 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".