Identification of QTL Involved in Low-temperature Tolerance at The Germination Stage by Recombination Inbred Lines in Rice
Bibliographic record
Abstract
Low-temperature germination (LTG) resistance of direct-seeded rice (DSR) seeds can overcome the developmental delay caused by low-temperature stress and ensure the vigorous growth of seedlings. The LTG is complex quantitative trait controlled by QTL s and has low genetic force, which leads to low efficiency of the direct selection of the trait by breeders. The research on QTL mapping for LTG in rice is helpful to carry out molecular marker-assisted selection (MAS) and improve the efficiency of selection for LTG in DSR breeding. In this study, the LTG rate showed extremely significant differences between the high-quality japonica rice variety Nanjing 46 in Jiangsu and the local variety Zhaxima in Yunnan. The QTL mapping was carried out by a recombination inbred line population (RIL) derived from a cross between Zhaxima and the Nanjing46. Total three QTL s ( qLTG-2 , qLTG-4 and qLTG-7 ) involved in LTG were detected on chromosome 2, 4 and 7, respectively. They accounted for 7.69%, 8.75% and 22.93% of the total phenotypic variation (PV), respectively. Interestingly, there was no reported QTL involved in LTG in the candidate region of qLTG-2 , which exhibited that the qLTG-2 was a novel QTL locus. The QTL s detected in this study will provide new genetic material and molecular markers for improving the low-temperature tolerance at germination stage in rice by molecular marker assisted selection breeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".