High-throughput Identification and Marker Development of Perfect SSR for Cultivated Genus of Passion Fruit (<i>Passiflora edulis</i>)
Bibliographic record
Abstract
Simple sequence repeat (SSR) markers are characterized by high polymorphism, good reproducibility and co-dominance etc. They can be easily applied to develop efficient, simple and practical molecular markers. In the present study, bioinformatics methods were applied to identify high-throughput perfect SSRs of cultivar Passiflora genome. A total of 13,104 perfect SSRs were obtained. SSR core sequence structure is mainly 2-4 bases, the maximum numbers are TA, AT, TC and AG. The maximum numbers of repetitions were up to 20 times. A total of 12,934 pairs of SSR markers were developed by using bioinformatics software, and 20 pairs of markers were selected for amplification specificity assessment of MTX and WJ10, and the polymorphism rate was as high as 60%. The large-scale development of the SSR markers of Passiflora cultivar has paved a foundation for the efficient utilization of the germplasm resources of passion fruit, genetic improvement of the varieties and molecular breeding.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".