Genetic Diversity Analysis of Capsicum Genus by SSR Markers
Bibliographic record
Abstract
The genetic diversity of pepper resources is rich and the potential of breeding is great. Therefore, the objectives of the study were to determine the genetic diversity and population structure of 32 accessions of Capsicum germplasm resources and contribute to breeding of pepper. In this study, the genetic diversity of different species of pepper germplasm was studied from the molecular level, which provided reference for the collection, research and rational utilization of pepper germplasm resources. 80 pairs of SSR primers were designed based on the whole genome coding region sequence of pepper. The 32 accessions of Capsicum germplasm resources of 12 species (subspecies) with different geographical origin and different traits were selected to screen 80 pairs of primers, which was to obtain clear bands, good stability of 40 SSR polymorphic primers. DPS, MEGA7 and POPGENE32 software were used to analyze the genetic diversity of 32 pepper germplasm resources. The results showed that 40 pairs of primers amplified 122 polymorphic bands, with an average of 3.05 loci amplified by each pair of primers, which showed that the SSR primers had high practicability in the genetic analysis of pepper. The mean value of effective allele number (Ne), observed heterozygosity (Ho), expected heterozygosity (He), shannon-weaver index (I), polymorphism information content (PIC) were showed that pepper genetic information is rich. Based on cluster analysis of UPGMA method and principal component analysis were basically consistent with the source of Capsicum were divided into 10 clusters.
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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 | 0.002 |
| 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".