Bibliographic record
Abstract
水稻籼粳亚种间存在着强大的杂种优势 ,但杂种育性普遍偏低成为这一杂种优势利用的主要障碍。研究证明 ,花粉不育是导致杂种不育的主要原因之一。张桂权和卢永根等 (1987- 1994 )鉴定了 6个花粉不育基因座位 (S a ,S b ,S c ,S d ,S e和S f)。其中S a基因座位已被庄楚雄等 (1996 )初步定位在水稻第一染色体着丝粒附近。本论文是在此基础上 ,利用美国Cornell大学、日本RGP构建的水稻高密度遗传图和日本构建的BAC/PAC物理图及其基因组测序资料 ,对S a作进一步的精细定位 ,建立了包含该基因的TAC重叠群 ,并通过序列分析发现S a候选基因 ,为分离鉴定S a基因及研究该基因在水稻杂种不育中的分子作用机理打下了基础。本研究主要结果如下 :1 构建了台中 6 5 (含S aj)及其近等基因系TILS4 (E4 ,含S ai)杂交的F2 群体 ,观察了 70 6株F2 个体的花粉育性分离状况。其中可育株 36 7株 ,半不育株 339株 ,分离比为 1∶1。2 利用前人筛选的多态性标记R2 15 9、R192 8和本研究新获得的多态性标记GR2、GR1、AR1、D2 3M、D1 5S、F12M 1,用 70 6株F2 群体对S a进行了的精细定位。结果表明S a与分子标记R2 15 9、GR2、GR1、AR1、R192 8、F12M 1、D1 5S和D2 3M之间的遗传距离分别为 2 0 7cM ,1 2 1cM ,0 6 5cM ,0 4 2cM
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".