Bibliographic record
Abstract
随着功能基因组学的发展，分子标记领域已开始从传统的随机或匿名性分子标记转向目的基因标记和功能性标记。目标起始密码子多态性（startcodon targeted polymorphism，SCOT）标记是一种基于翻译起始位点的目的基因标记新技术，具有操作简单、重复性好等特点，根据ATG翻译起始位点侧翼保守序列来设计单引物，扩增产生偏向候选功能基因区显性多态性标记，适合不同层次实验室的不同领域的广泛应用，SCoT标记已开始在水稻和花生上得到运用。本文旨在介绍给研究者一种可以作为传统的RAPD、ISSR标记有效补充的目的基因标记新技术，希望其广泛的应用于生物多样性分析、遗传图谱构建、重要性状标记等方面。
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".