Bibliographic record
Abstract
为了建立油松cDNA SRAP-PCR反应体系,利用正交设计L16（45）对PCR反应体系的Taq酶、模板cDNA、引物、Mg2+、d NTP这五个因素在四个水平上进行优化。使用SPSS软件对PCR结果结合方差分析和直观分析。结果表明:不同因素水平的变化对SRAP-PCR反应的影响大小依次为d NTP>模板c DNA>Taq酶>引物>Mg2+。筛选各因素最佳水平,油松c DNA SRAP-PCR最佳反应体系（20μL）:Taq酶2 U,c DNA模板用量80120 ng,引物浓度0.4μmol/L,Mg2+浓度1.5 mmol/L,d NTP浓度0.2 mmol/L。这一优化体系的建立有助于今后利用cDNA-SRAP技术对油松进行基因表达差异的分析。
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".