Simple sequence repeat analysis of a clonally propagated species: A tool for managing a grape germplasm collection
Bibliographic record
Abstract
The USDA germplasm repositories help to preserve the genetic variability of important crop species by collecting and maintaining representative cultivars and related germplasm. Simple sequence repeat markers with high allelic diversity were used to type 41 grapevines from 40 accessions. All vines were either seedless table grape cultivars or cultivars with names similar to table grape cultivars. The proportion of shared alleles was selected as the most appropriate statistical measure of genetic distance for this population. In conjunction with morphological traits, known synonyms were confirmed and a previously unknown synonym was discovered. An alleged synonym in the literature was disproved by the DNA data. The data were consistent with known parentage, where such data were available. Two mislabeled vines in the USDA collection were identified. UPGMA grouped the cultivars loosely into three groups: a group of nine mostly Middle Eastern cultivars, a group of 22 accessions mostly from Russia and Afghanistan that were morphologically similar to 'Thompson Seedless', and a third very loose group of 11 accessions consisting mostly of eastern European wine grape cultivars. The limitations and usefulness of this type of analysis are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".