Genetic Diversity of 30 Cai-xins (<em>Brassica rapa var. parachinensis</em>) Evaluated Based on AFLP Molecular Data
Bibliographic record
Abstract
Cai-xin is common Chinese name for Brassica rapa var. parachinensis that is one of very important leafage vegetables in South China. The objectives of this research were to detect genetic diversities of the selected 30 Cai-xins based on AFLP makers as well as to evaluate the feasibilities of AFLP approach for biodiverse study . In this paper, the 25 pairs of AFLP primers were employed to generate 1160 amplified bands, of which 876 bands account for 76% were polymorphic bands The average polymorphism of tested 25 primer combinations reached 80%, the mean of polymorphism information content (PIC) was about 0.0239 and the amount of polymorphic loci was about 85.33%. The parameters of genetic diversity were calculated by the aid of GenAlEx 6.4 software including the number of different alleles (Na) 1.754, the number of effective alleles (Ne) 1.544, Shannon's Information Index (I) 0.472 and He 0.363. The values of genetic distance (GD) and genetic similarity (GS) were 0.112 and 0.895, respectively. Statistic analysis revealed that almost 100 percentage of variation existed within Cai-xins based on AMOVA data. The tested Caixins can classified into four groups at the 0.17 threshold of Nei’s genetic distances by clustering analysis based on UPGMA approach . The present results indicated that the genetic diversity of the tested Cai-xins should be quite low and the genetic variations of Caixins be mostly attributed to within varieties. Whereas we confidentially have conclusions that AFLP approach might be useful, efficiency and accuracy to detect genetic diversity among varieties, landraces and lines of Caixin, especially for those which have close relationship.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".