A simplified procedure for verifying and identifying potato cultivars using multiplex PCR
Bibliographic record
Abstract
Correct identification of potato cultivars and selections is essential to a large and diverse user group. This group includes curators of germplasm repositories, breeders and other researchers, certification program officials, commercial growers, processing industry managers and for some cultivars, the public. Agencies involved in cultivar registration and plant breeders' rights (or patenting) also have a vested interest in correct identification. DNA fingerprinting is an important tool that can be used to describe new or existing cultivars, verify cultivar identity, and resolve cultivar mixtures. Gel-based fingerprints are usually preferred because they are visual and within the technical capacity of most molecular laboratories. In this study, a multiplex PCR protocol "Multiplex SUP" and an improved version "Multiplex SUPN" were developed using four primer pairs (STEM0014 and genes of starch synthase, patatin, and UDP-glucose pyrophosphorylase). The agarose-gel-based Multiplex SUP was successfully used in identifying cultivars from blind samples in a collaborating laboratory, and in pilot tests to verify the identity of introduced cultivars for seed potato production. The Multiplex SUPN, using native polyacrylamide gel electrophoresis (PAGE) with GelRed or ethidium-bromide staining, generated more than 38 polymorphic markers among the potato cultivars tested. The method distinguished 116 cultivars that included many of the public potato cultivars registered in Canada and several protected cultivars that were fingerprinted with permission. The Multiplex SUPN-PAGE method is user friendly and effective, and is recommended for routine potato cultivar verification and identification. Key words: cultivar identification, database development, DNA fingerprinting, GelRed staining, genotyping, polymorphism, Solanum tuberosum L.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".