Evaluation of amplified fragment length polymorphism and simple sequence repeats for tomato germplasm fingerprinting: utility for grouping closely related traditional cultivars
Bibliographic record
Abstract
Cultivated tomato (Solanum lycopersicum L.) germplasm shows limited genetic variation. Many DNA marker systems have been used for genetic diversity studies in wild and cultivated tomatoes, but their usefulness for characterizing phenotypic differences among very closely related cultivars remains uncertain. We have used 19 selected simple sequence repeat (SSR) markers and 7 amplified fragment length polymorphism (AFLP) primer combinations to characterize 48 cultivars of tomato, mainly traditional cultivars from the south-east of Spain. The main types were Solanum lycopersicum L. 'Muchamiel', 'De la pera', and 'Moruno'. The robustness of the dendrograms and the discrimination power reached with each marker type were similar. Unique fingerprinting even of the most closely related tomato cultivars could be obtained using a combination of some SSR and AFLP markers. A better grouping of the 'Muchamiel' cultivars was observed with SSR markers, whereas the grouping of cultivars of 'De la pera' type was best achieved with AFLPs. However, both types of markers adequately grouped cultivars of the main types, confirming the utility of SSR and AFLP markers for the identification of traditional cultivars of tomato.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".