<b>Assessing salt tolerance, phenotypic traits and genetic diversity in chickpea (</b> <i> <b>Cicer arietinum L.</b> </i> <b>) accessions using SSR markers</b>
Bibliographic record
Abstract
Soil salinity is a major abiotic stress that severely limits chickpea (Cicer arietinum L.) productivity, especially in semi-arid regions such as Uzbekistan. This study evaluated 50 chickpea accessions under optimal and naturally saline field conditions, combining phenotypic analysis with SSR marker-based genetic diversity and in silico mapping to identify tolerant germplasm. Significant phenotypic variation was observed and several genotypes (e.g., ‘Malxotra’, ‘Guliston’, ‘Lazzat’, ‘Iftikhor’, ‘SSA−2’, ‘SSA−10’) were identified as highly salt-tolerant. Under salinity stress, seed weight per plant showed strong positive correlations with pod number (r = 0.63***) and seed number (r = 0.69***). Genetic diversity assessed using 37 polymorphic SSR markers revealed 148 alleles, averaging 3.8 alleles per locus. The mean polymorphism information content (PIC) was 0.37 (ranging from 0.21 to 0.63), with the highest expected heterozygosity (He = 0.69) detected for markers H1C22, STMS22, and TR20. In silico analysis localized these markers to salt-tolerance associated regions on chromosomes, identifying candidate genes encoding LEA proteins, ion transporters, kinases, and redox regulators. These fundings, particularly the identified tolerant genotypes and associated markers, provide a valuable foundation for marker-assisted selection and the development of salt-tolerant chickpea cultivars.
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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) | 0.000 | 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".