Bibliographic record
Abstract
用一套分别含有不同抗叶锈基因的53个以Thatcher为遗传背景的近等基因系（near-isogeniclines,NILs）对已报道的分别与抗叶锈基因Lr24和Lr35连锁的STS、SCAR进行特异性验证。结果对于与Lr24连锁的STS标记,在53个NILs中只在TcLr24亲本中扩增出片段大小与报道相同的310bp的条带,在TcLr35中也扩增出了一条片段,但片段大小不同于310bp约为270bp。对于与Lr35连锁的SCAR标记,只在TcLr35亲本中扩增出片段大小为900bp的条带,与报道片段大小一致。验证结果表明与抗病基因Lr24和Lr35连锁的STS、SCAR分子标记在NILs中特异性都较好,进一步证明了这两个分子标记可方便地用于小麦抗叶锈基因Lr24、Lr35的分子标记辅助选择育种。
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".