Evaluation of AFLPs for germplasm fingerprinting and assessment of genetic diversity in cultivars of tomato (<i>Lycopersicon esculentum</i> L.)
Bibliographic record
Abstract
Cultivated tomato (L. esculentum L.) germplasm exhibits limited genetic variation compared with wild Lycopersicon species. Amplified fragment length polymorphism (AFLP) markers were used to evaluate genetic variation among 74 cultivars, primarily from California, and to fingerprint germplasm to determine if cultivar-specific patterns could be obtained. All 74 cultivars were genotyped using 26 AFLP primer combinations; of the 1092 bands scored, 102 AFLP bands (9.3%) were polymorphic. Pair-wise genetic similarity coefficients (Jaccard and Nei-Li) were calculated. Jaccard coefficients varied from 0.16 to 0.98 among cultivar pairs, and 72% of pair-wise comparisons exceeded 0.5. UPGMA (unweighted pair-group method with arithmetic averaging) clustering and principle component analysis revealed four main clusters, I-IV; most modern hybrid cultivars grouped in II, whereas most vintage cultivars grouped in I. Clusters III and IV contained three and two cultivars, respectively. Some groups of cultivars closely related by pedigree exhibited high bootstrap values, but lower values (<50%) were obtained for cluster II and its four subgroups. Unique fingerprints for all 74 cultivars were obtained by a minimum of seven AFLP primer pairs, despite inclusion of some closely related cultivars. This study demonstrated that AFLP markers are effective for obtaining unique fingerprints of, and assessing genetic diversity among, tomato cultivars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".