Development of Polymorphic SSR Markers in Chinese Water Chestnut Based on RAD-seq
Bibliographic record
Abstract
Chinese water chestnut ( Eleocharis dulcis ) is an important characteristic vegetable that grows in shallow waters. In order to develop simple sequence repeat (SSR) molecular markers for genetics research on Eleocharis dulcis , simplified genome sequencing by restriction-site associated DNA sequencing (RAD-seq), SSR search and primer design were performed. A total of 5039 SSR loci were detected, from which 4 137 pairs of primer were designed. Among them, the proportion of tri-nucleotides motifs was the highest (1 894), accounting for 45.89% of the total loci; followed by di-nucleotide with 1 406, accounting for 34.07%.. A total of 100 pairs of randomly selected primers were verified. 93% of the primers were successfully amplified. To assess the polymorphism of these primers, two varieties of Chinese water chestnuts were used to amplify the SSR marker using 83 pairs of primers. The 83 pairs of primers generated a total of 232 fragments. A total of 128 fragments were polymorphic. 60 pairs of primers out of 83 pairs generated at least one polymorphic band, and the ratio of polymorphic primers was 72.28%. These SSR markers developed from RAD tags were proved to be effective, which will provide a useful tool for genetic diversity research and molecular breeding in Chinese water chestnuts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".