Assessment of Variability and Identification of Transgressive Segregants for Yield and Yield Component Traits in Early Segregating Generations of Chickpea
Bibliographic record
Abstract
In order to compute the genetic variability, heritability and genetic advance an investigation was carried out with 575 plants of F 2 population and F 2 derived F 3 progenies from the cross between ICC 13124 and WR315 of chickpea ( Cicer arietinum L.). The genotype ICC 13124 is tolerant to drought but susceptible to wilt, while, WR 315 is resistant to wilt and relatively less tolerant to drought. Considerably high variability was observed in 575 plants of F 2 and F 2-3 progenies . The phenotypic variance was higher than the corresponding genotypic variance for all the characters. Environmental influence was very meager in expression of most of the traits which is evident from narrow difference between Genotypic Coefficient of variation (GCV) and Phenotypic Coefficient of Variation (PCV) estimates. Heritability estimates in broad sense was high for all the characters under study in both F 2 and F 3 coupled with high genetic advance as per cent over mean indicated the presence of additive gene action for these traits. The crosses had thrown a good number of transgressive segregants over better parent for seed yield per plant. More number of transgressive segregants was found for number of seeds per plant followed by number of pods per plant and yield per plant. A track on these transgressive segregants should be maintained and forwarded to further generation till they reach nearly homozygous condition. Most promising one can be used in further breeding programme.
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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 | 0.001 | 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".