SRAP Markers and Morphological Traits Could Be Used in Test of Distinctiveness, Uniformity, and Stability (DUS) of Lettuce (Lactuca sativa) Varieties
Bibliographic record
Abstract
The test of distinctiveness, uniformity, and stability (DUS) is a necessary step for variety identification and new variety application. The objective of this study is to provide molecular marker-assisted approach combined with morphological trait-based testing for more convenient and fast DUS test and identification of varieties. Eighteen pairs of SRAP markers and 40 morphological traits for DUS test were applied for genetic diversity analysis of 50 lettuce (Lactuca sativa) varieties. Average polymorphism information content (PIC) of the SRAP markers was 0.80, ranging from 0.39 to 0.97. Cluster analysis using UPGMA of the band patterns amplified by SRAP marker and morphological trait-based clustering separated the varieties into three groups. The correlation coefficient of SRAP marker and morphological traits was 0.5455 reflecting that the two clustering results shared some similarity and consistence. It revealed that the combination of both SRAP marker and morphological trait analysis is more conducive to proper identification and classification of plant varieties, which will undoubtedly bring an alternative choice to DUS testing of plant new varieties and conservation of plant germplasm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".