Inter Simple Sequence Repeat Fingerprints for Assess Genetic Diversity of Tunisian Garlic Populations
Bibliographic record
Abstract
Garlic (Allium sativum L.) that is cultivated in Tunisia is heterogeneous and unclassified with no registered local cultivars. At present, the level of genetic diversity in Tunisian garlic is almost unknown. Inter Simple Sequence Repeats (ISSR) genetic markers were therefore used to assess the genetic diversity and its distribution in 31 Tunisian garlic accessions with 4 French classified clones used as control. It was the first time that ISSR markers were used to detect diversity in garlic. Seventeen ISSR primers were screened; seven primers detected 73 polymorphic bands. A high level of polymorphic loci (p) was found in Tunisian populations (54%). Nei’s total genetic diversity coefficient was 0.45 and 0.34 respectively for Tunisian and French garlic. Genetic distances observed between Tunisian accessions, ranged between 38.4 and 78.1%. Factor analysis of distances’ table (AFTD) did not classify accessions on the base of geographical origin or morpho-physiological characters, particularly bolting ability, but confirmed the appurtenance of analyzed accessions to sativum botanical subspecies. There was sufficient diversity detected to start a national collection of garlic germplasm which is crucial for the conservation of genetic diversity and its valorization. Keywords: Allium sativum L., ISSR markers, genetic diversity, Tunisian garlic populations.
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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 | 0.002 | 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".