QTL detection for rice grain quality traits using an interspecific backcross population derived from cultivated Asian (<i>O. sativa</i>L.) and African (<i>O. glaberrima</i>S.) rice
Bibliographic record
Abstract
An interspecific advanced backcross population derived from a cross between Oryza sativa "V20A" (a popular male-sterile line used in Chinese rice hybrids) and Oryza glaberrima (accession IRGC No. 103544 from Mali) was used to identify quantitative trait loci (QTL) associated with grain quality and grain morphology. A total of 308 BC3F1 hybrid families were evaluated for 16 grain-related traits under field conditions in Changsha, China, and the same families were evaluated for RFLP and SSR marker segregation at Cornell University (Ithaca, N.Y.). Eleven QTL associated with seven traits were detected in six chromosomal regions, with the favorable allele coming from O. glaberrima at eight loci. Favorable O. glaberrima alleles were associated with improvements in grain shape and appearance, resulting in an increase in kernel length, transgressive variation for thinner grains, and increased length to width ratio. Oryza glaberrima alleles at other loci were associated with potential improvements in crude protein content and brown rice yield. These results suggested that genes from O. glaberrima may be useful in improving specific grain quality characteristics in high-yielding O. sativa hybrid cultivars.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".