Development of EST-SSR markers in <i>Bergenia ciliata</i> using <i>de novo</i> transcriptome sequencing
Bibliographic record
Abstract
Bergenia ciliata (Haw.) Sternb. is an important herb predominantly found in the Indian Himalayan Region. It is widely used in medicines, healthcare systems, cosmetics, fodder, and ornamental purposes. The Illumina sequencing and de novo transcriptome assembly were carried out in B. ciliata to develop and identify simple sequence repeat markers. A total of 18 226 simple sequence repeats (SSRs) were identified wherein di-nucleotides were found to be abundant (47.88%), followed by mono-nucleotide (35.03%) and tri-nucleotide (15.88%) repeats. A total of 11 839 EST-SSR primers were designed, of which 96 primer pairs were commercially synthesized. Finally, 17 primer pairs revealed clear, distinct polymorphic bands, and these primers were validated with 40 diverse B. ciliata accessions. The present study revealed moderate level of genetic diversity ( Ho = 0.389, He = 0.542, and PIC = 0.513). Furthermore, the transcriptome data and EST-SSR markers generated during the present investigation could be an important genetic resource for functional genomics, population studies, and conservation genetics of the genus Bergenia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".