Development of new set of microsatellite markers in cultivated tobacco and their transferability in other Nicotiana spp.
Bibliographic record
Abstract
Scarcity of molecular markers in tobacco has been a limitation, hampering the acceleration of breeding efforts. Development of microsatellite markers is a prerequisite for mapping, tagging of many useful qualitative and quantitative traits and also for the generation of saturated linkage map. Use of microsatellite-enriched genomic libraries an efficient and rapid method for the identification of clones harboring microsatellite motifs leading to the development of microsatellite markers. In the present study, a total of 111 microsatellite motifs was identified from the enriched library, of which, 70 motifs (which includes perfect and imperfect repeat) were used for marker development. These newly developed markers could successfully differentiated different types of tobacco and diverse cultivars of Flue Cured Virginia (FCV) tobacco. The high rate of transferability (95-7% - 100%) of these microsatellite markers in a wide range of Nicotiana species indicated their potential as viable resources in the inter-specific gene transfer programme. The set of microsatellite markers developed in this study is a valuable addition to the already available DNA marker resources in tobacco.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".