Using Amplified Fragment Length Polymorphism Markers to Confirm Identity and Correct Labeling of Japanese Barberry (Berberis thunbergii) Cultivars in the Market
Bibliographic record
Abstract
Japanese barberry ( Berberis thunbergii DC.) is a popular ornamental shrub used in garden and urban landscaping. Currently there are over 60 B. thunbergii cultivars in the market. To better distinguish its cultivars, we used the amplified fragment length polymorphism (AFLP) technique to develop DNA marker profiles for 59 cultivars and hybrids. These markers were used to authenticate the trueness-to-name of B. thunbergii cultivars in production and in the market, control for intracultivar genetic variants, and develop a molecular key to identify cultivars approved for importation in Canada. Polymorphic markers from seven primer combinations were able to clearly differentiate 57 of 59 cultivars evaluated. Two cultivars, Aurea and Aurea Nana, could not be differentiated because they had identical marker profiles. Among the 274 plants tested, 263 were confirmed to be true-to-name and correctly labeled, whereas 11 plants could not be confirmed true-to-name. Seven of the 20 cultivars evaluated exhibited detectable intracultivar genetic variation. ‘Crimson Pygmy’ had the highest number of plants exhibiting genetic variability. Overall, nursery producers and retailers do not appear to be mixing or mislabeling cultivars. A molecular key developed from a subset of 25 markers was able to accurately identify and differentiate the 11 B. thunbergii cultivars approved for importation in Canada. This key could be used in a cultivar verification program to facilitate international trade of B. thunbergii cultivars where wheat rust is a concern.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".