Bibliographic record
Abstract
本研究利用ISSR和EST-SSR标记对中国13个、日本4个与肯尼亚5个茶树品种的遗传多样性进行分析,重点比较了ISSR和EST-SSR在多态性位点数、引物解析能力、多态性信息含量和标记系数等方面的差异。结果表明在位点检测能力上ISSR明显高于EST-SSR,每条ISSR引物可检测的平均条带数（12.5）比每对EST-SSR引物（3.1）高三倍。ISSR标记的Rp值是EST-SSR标记的6.3倍,其多态性信息含量（PIC）和标记系数（MI）也明显高于EST-SSR,这表明ISSR具较高的多态性位点检测能力和较高的标记效率。EST-SSR揭示的Nei基因多样性（H=0.28）高于ISSR（0.21）,这种差异进而会影响到遗传距离和聚类分析的结果。在检测中国、日本和肯尼亚茶树品种的遗传差异上,两种标记表现出较一致的趋势,中国茶树品种在多态性位点数量、Nei基因多样性、遗传距离上均大于日本和肯尼亚的茶树品种。研究还表明,由于EST-SSR可检测到等位位点的变异,它比ISSR可以更准确地反映和揭示供试品种的遗传多样性水平。同时由于单个EST-SSR标记检测到的位点数量较ISSR标记少,且特异性较强,谱带较易分辨,因此比ISSR标记更适用于对大量样本的分析。
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".