Molecular Marker Assisted Breeding of a New <i>Japonica</i> Hybrid Rice
Bibliographic record
Abstract
The development of Japonica hybrid rice is of great significance to ensure supply of good quality Japonica rice and food security in China. Aiming at the problems of late growth period, unstable yield, and the need to improve rice quality and seed production yield of Japonica hybrid rice, a new Japonica hybrid rice combination 'Shenyou28' with early maturity, good quality, blast resistance, stable and high yield and easy seed production was developed by using molecular breeding techniques including high-density rice gene chip. 'Shenyou28', bred by Shanghai Academy of Agricultural Sciences, is a three-line Japonica hybrid rice with 'Shen21A' as sterile line and 'Shenhui26-28' as restorer line. For two-year regional trials from 2018 to 2019, the average output were 10 588.5 kg/hm 2 , which was 11.6% higher than the control variety 'Huayou14'. For the one-year production trial, the output reached 10 473.0 kg/hm 2 , which was 4.8% higher than the control variety 'Huayou14'. 'Shenyou28' has good quality, reaching the second grade of national standard, and has fragrance because it carries fragrance gene badh2-p-5 ’ UTR . It also has good resistance to rice blast and bacterial blight because it aggregates the rice blast resistance genes Pi2 , Pita , Pib , Pi9 , Pi54 , Pikm , Pit , and bacterial blight resistance gene Xa21 . It was approved by the Crop Variety Certification Committee of Shanghai in 2020. The breeding process, variety characteristics and key points of high yield mechanized seed production technology of 'Shenyou28' are introduced in this study.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".