Genetic Diversity of Rapeseed Accessions from Different Geographic Locations Revealed by Expressed Sequence Tag‐Simple Sequence Repeat and Random Amplified Polymorphic DNA Markers
Bibliographic record
Abstract
ABSTRACT Genetic diversity information will be very valuable for future rapeseed (Brassica napus L.) improvement. The genetic diversity and relationships among 92 rapeseed accessions, including 44 from China, 22 from Europe, 16 from the United States, and 10 from Canada, were assessed by 60 random amplified polymorphic DNA (RAPD) and 22 expressed sequence tag (EST)‐simple sequence repeat (SSR) primers. In total, 618 RAPD and 117 EST‐SSR polymorphic fragments were detected. The average number of polymorphic fragments found by each RAPD primer was 10.3 ranging from 3 to 17 and that detected by each pair of EST‐SSR primer was 5.3 ranging from 4 to 7. The unweighted pair‐group method with arithmetic mean (UPGMA) cluster analysis revealed that these 92 accessions could be classified into three major clusters. Cluster I consisted of accessions mainly from China, which belong to the semi‐winter type. Cluster II contained accessions from Europe and the United States, which belong to the winter type. Cluster III was a semi‐winter and spring type mixture group, which contained accessions mainly from China and Canada. The principal component analysis and population structure analysis revealed similar results to the cluster analysis. Analysis of molecular variance result based on four geographic groups indicated that genetic variation was 8.24% among populations of geographic regions and 91.76% within geographic regions. Rapeseed accessions from the United Stated have the greatest genetic distance from accessions of other geographic origins, especially those from China. United States rapeseed could be important germplasm resources for enriching the genetic background of Chinese rapeseed and vice versa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".