Rice Quality Evaluating and Key Quality Genes Genotyping of Rice Germplasm Resources from Africa and Brazil
Bibliographic record
Abstract
High quality is one of the most objectives in rice breeding, and introducing and evaluating the quality of rice germplasm are very important for screening novel parent with high quality and breeding high quality varieties. In this study, a total of 28 rice germplasm from Africa and Brazil were evaluated their milling quality, appearance quality and cooking and eating quality in Wuhan City, Hubei Province. The results showed the milling quality and appearance quality of most varieties were very high, but the cooking and eating quality of them were bad. Finally, 13 varieties with high quality were screened. The correlation analysis indicated that different traits among milling quality, appearance quality and cooking and eating quality had low correlation between each other. Further, all germplasm was genotyping three important quality genes, GS3 , Wx and ALK . There were two genotypes in each of GS3 and Wx , and ALK had three genotypes. The phenotypes showed significantly difference between different genotypes for both Wx and ALK . Our results will give valuable germplasm resources, gene resources and marker resources for breeding high quality 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".