Analysis of SSR Information in Aquatic Plant <i>Euryale ferox</i> Salisb Transcriptome
Bibliographic record
Abstract
In order to study the genetic diversity breeding of aquatic plant Euryale ferox Salisb, this paper developed Euryale ferox Salisb EST-SSR marker by transcriptome sequencing technology, and 86 829 Unigenes were screened. Then we got 9 640 SSR loci from transcriptome by using the software MISA, accounting for 11.10% of all the Unigenes, the average distributing distance is about 7.65 kb. The Di and Trinucleotide are the major repetitive types in Euryale ferox Salisb transcriptome, accounting for 61.88% and 21.55% of all the SSRs, respectively. There are 226 repeat motifs were detected, of which A/T, AG/CT and AAG/CTT were high frequency repeat motifs, accounting for 1.25%、50.87% and 6.51% of all repeat motifs, respectively. The SSR motif length is centered in 12~20 bp, while the number of SSR over 20 bp was 717, accounting for 7.44%. 11 pairs of SSR primers were successfully designed and screened. The results showed that the SSR loci of the Euryale ferox Salisb transcriptome were higher in frequency, rich in type, which could serve as foundation for the further development of SSR markers and genetic diversity.
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 | 0.001 |
| 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) | 0.001 | 0.001 |
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".