Genetic variation among lentil (<i>Lens culinaris</i> Medik) landraces from Southeast Turkey
Bibliographic record
Abstract
Abstract The molecular characterization of cultivated plant genepools is of foremost importance for germplasm utilization in plant breeding. However, no comprehensive genetic fingerprinting of Turkish lentil landraces existed so far. To overcome this gap, 38 lentil landraces from southeast Turkey, together with six commercial varieties, were molecularly characterized using inter simple sequence repeat (ISSR) and amplified fragment length polymorphism (AFLP) molecular markers. The ISSR analysis, performed with 14 primers, yielded 105 polymorphic bands and the AFLP analysis, carried out with six primer combinations, amplified 119 polymorphic fragments. Even though the AFLP produced more bands per primer combinations, the ISSR detected more polymorphisms. Unweighted pair‐group method with arithmetic means dendrograms based on Jaccard similarities obtained from three data sets: (i) ISSR, (ii) AFLP and (iii) combined ISSR and AFLP data, were similar and separated the landraces into two main groups. Turkish lentil landraces exhibited considerable genetic diversity. One landrace from Karacadag/Diyarbakir region was significantly different from the rest of the germplasm analysed. Jaccard distances highlighted sharp differences among landraces over short geographic distances. The knowledge of regional differentiation has practical utility in the management of germplasm and in breeding programmes.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".