Genetic effects of major QTLs controlling low-temperature germinability in different genetic backgrounds in rice (<i>Oryza sativa</i>L.)
Bibliographic record
Abstract
The effects of QTLs are demonstrated basically within the population used in the original QTL analysis as the difference between the alleles of the parental varieties. For the efficient use of QTLs in breeding programs, it is necessary to assess whether the QTL exhibits its genetic effect when it is introgressed into different genetic backgrounds. Extensive studies of tolerance to low temperature at the seed germination stage (called low-temperature germinability) in rice revealed that 2 major QTLs on chromosomes 3 and 11, qLTG3-1 and qLTG11, have large effects. This study assessed the effects of these 2 QTLs from an aus variety, Kasalath, in different genetic backgrounds of 3 japonica varieties, Hoshinoyume, Hayamasari, and Koshihikari. Backcrossed progenies and chromosome segment substitution lines showed that both qLTG3-1 and qLTG11 were effective in the Hoshinoyume and Koshihikari backgrounds, while only qLTG3-1 was effective in the Hayamasari background. The results in this study demonstrated that these 2 QTLs are useful for the improvement of low-temperature germinability in rice breeding programs. The results also indicated that low-temperature germinability in rice is governed by an epistatic interaction of qLTG11.
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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".