Assessment of genetic diversity and relationships based on RAPD and AFLP analyses in <i>Miscanthus</i> genera landraces
Bibliographic record
Abstract
Qin, Y., Kabir, M. A., Wang, H. W., Lee, Y. H., Hong, S. H., Kim, J. Y., Yook, M. J., Kim, D. S., Kim, C. S., Kwon, H. and Kim, W. 2013. Assessment of genetic diversity and relationships based on RAPD and AFLP analyses in Miscanthus genera landraces. Can. J. Plant Sci. 93: 171–182. In this study, molecular markers, random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) as well as combined RAPD and AFLP analysis were used to assess genetic diversity in a reference set of 38 Miscanthus accessions of which 32 were collected from South Korea and 6 from foreign countries. Using 30 selected RAPD primers, 197 amplified products were generated with an average of 6.6 bands, of which 135 bands were polymorphic (68.6%). A total of 1150 bands were detected by four-primer AFLP combinations with an average of 287.5 bands, out of which 923 bands were polymorphic (80.3%) across all the accessions. Analysis of molecular variance (AMOVA) indicated that a high proportion of the genetic variation (56% for RAPD and 58% for AFLP) was found among the Miscanthus species in South Korea. Genetic relationship was estimated using the Jaccard's coefficient values between different accessions, ranging from 0.23 to 0.93 in RAPD and 0.34 to 0.94 in AFLP. The un-weighted pair group method with arithmetic mean (UPGMA) analysis demonstrated less difference between RAPD and AFLP when alternative similarity coefficient was applied. The principal coordinates (PCO) analysis also revealed the significant geographic structure in the tested accessions. Among the accessions, SNU-M-040, 074 and 157 were highly divergent. Pattern of isolation by distance was observed in Miscanthus accessions, indicating that significant genetic differentiation among accessions might be due to the geographic distance.
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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 | 0.003 | 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".