Bibliographic record
Abstract
近年来,利用与抗病基因紧密连锁的分子标记进行辅助选择已经成为抗病育种的重要手段。本研究利用显性标记ST10对24个水稻品种以及24个水稻品种中的镇稻88/武育粳3号和武运粳7号/徐稻3号的2个F2群体进行检测。PCR结果显示,17个抗病品种有8个能扩增出约727bp的目标片段;在镇稻88/武育粳3号杂交组合中,感病亲本武育粳3号和F2群体中的8个感病单株均不能扩增出目标片段,抗病亲本镇稻88、F1和13个不感病单株中的11株都能够扩增出目标片段;用于检测的武运粳7号/徐稻3号的F2群体中,8株感病单株没有检测出目标片段,而13株不感病的单株有9株扩增出目标片段。结果表明,ST10与条纹叶枯病抗性基因Stv-b^i紧密连锁,表现为共分离。
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".