Variation in Seed‐Quality Traits of Chickpea and Their Correlation to Raffinose Family Oligosaccharides Concentrations
Bibliographic record
Abstract
Genetic resources with desired seed composition are needed to improve nutritional quality of chickpea (Cicer arietinum L.) seeds. A germplasm collection of 171 chickpea genotypes (desi and kabuli types) was characterized for selected seed quality traits (thousand‐seed weight [TSW], starch, protein, and amylose) in one greenhouse and two field trials. Kabuli‐type chickpea genotypes (115.7 to 537.4 g and 36.2 to 49.0%) had higher TSW and starch concentrations than desi types (114.6 to 332.4 g and 32.4 to 42.9%), respectively. Desi type chickpea genotypes (16.7 to 27.5%) showed a higher range for protein concentration than kabuli types (17.1 to 24.8%). However, amylose concentration did not vary significantly between desi (29.7 to 34.4%) and kabuli (29.2 to 35.0%) type chickpea genotypes. Genotype, environment, and their interaction showed a significant impact on selected seed‐quality traits. Among the chickpea seed‐quality traits studied, seed weight was the most heritable trait, and it showed significant positive correlation with starch concentration. Protein, amylose, and total raffinose family oligosaccharides (RFO) had significant negative correlation with TSW. However, total RFO concentration showed significant positive correlation to both starch and protein concentrations. The identified desi and kabuli genotypes can be used as new genetic resources in chickpea improvement programs to develop chickpea varieties with enhanced nutritional composition.
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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 | 0.000 |
| 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".