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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".