Study of genetic diversity in rapeseed (Brassica napus L.) genotypes using microsatellite markers
Bibliographic record
Abstract
Oilseed crops such as canola have important role to produce oil and energy needed for human. Information about genetic variability based on different markers, particularly molecular markers has played a key role in designing breeding programs. To study the genetic diversity in 24 genotypes of rapeseed, the 10 microsatellite primers were used according to previous studies. The results showed that the average polymorphic information content for assessing primers was 0.55 and the mean observed and expected heterozygosity for all primers, were 0.35 and 0.41 respectively. NA12-E09 locus had the highest rate (1.0893) of Shannon diversity index, which represents diversity among the population, whereas loci RA2-A11 and OL10-G06 had the lowest Shannon index. Cluster analysis based on molecular data using Jaccard's similarity coefficient and UPGMA method, grouped rapeseed varieties into the third major group. Accordingly, CR3133 variety of Canada origin which is located in a separate group showed lower similarity compared with other varieties. In general, rapeseed genotypes showed intra-species diversity based on microsatellite markers could be used in plant breeding programs
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 | 0.002 | 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".