Bibliographic record
Abstract
TRAP是一种新的基于PCR的植物基因型标记技术,具有简单、稳定、效率高的特点.借助日益增长的庞大的生物序列信息,TRAP利用生物信息工具和EST数据库信息,产生目标候选基因区多态性标记.TRAP技术采用两个18核苷酸引物产生标记.一个为固定引物,依据EST序列设计;另一个为随机引物,针对外显子和内含子的特点,设计为分别富含GC或AT核心区的任意序列.PCR扩增前5个循环采用35℃的退火温度,后35个循环采用50℃的退火温度.对不同的植物种类,每一个PCR反应可产生多达50个可统计DNA片段.本文在阐述了TRAP的原理与流程后,对该技术的优势和应用情况进行了总结,并对其前景做了展望.
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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".