Rapid and targeted introgression of <i>fgr</i> gene through marker-assisted backcrossing in rice (<i>Oryza sativa</i> L.)
Bibliographic record
Abstract
While it is crucial for developing countries like Malaysia to achieve self-sufficiency in rice (Oryza sativa L.), it is equally critical to be able to produce high-quality rice, specifically fragrant rice, which demands are often met through importation. The present study was aimed at developing high-yielding fragrant rice, in a timely and cost-effective manner. A marker-assisted backcross (MABC) approach was optimised to introgress the fragrance gene (fgr) into two high-yielding Malaysian varieties, MR84 and MR219, within two years utilising less than 50 molecular markers. Coupled with phenotypic screening, one single foreground marker (fgr-SNP) and 48 background markers were selected and utilised, revealing recovery of at least 90% of recurrent parent genome (RPG) in merely two backcross generations. Collectively, the yield potential of the developed BC2F2 lines (BLs) was higher (P > 0.05) than the donor parent, MRQ74, and similar (P < 0.05) to both the recurrent parents, MR84 and MR219. In addition, some of the developed BLs showed good grain quality, such as having long grain. We believe that this is the first report comprising the validation and utilisation of the single functional marker system (fgr-SNP) in introgressing the fgr gene into different rice varieties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".