Heritability and predicted gain from selection in components of crop duration and seed yield in sesame (<i>Sesamum indicum </i>L.)
Bibliographic record
Abstract
The duration of maturity in sesame is dependent on several physiological and phenological variables, which are interrelated and could be manipulated separately in breeding programme. For effective manipulation of these traits, knowledge of genetic architecture is prerequisite. Therefore, seventy diverse sesame genotypes were studied to know the heritability and predicted gain for components of crop duration, correlation among themselves and to identify superior genotypes to be utilized in future breeding programmes. Sizeable variability was revealed among genotypes for studied traits. Genotypic coefficient of variation (GCV) was high (>20%) for seed yield and capsules per plant with high heritability (>80%) and high genetic advance as pecentage of mean (>20%). Also, reproductive period and seeds per capsule expressed high heritability coupled with high genetic gain and moderate GCV. All these key components seem to be under the control of additive gene action, which is fixable. Large environmental effect for primary branches per plant was detected. Correlation of capsules per plant was significant positive and physiological maturity was significant negative with seed yield per plant, but both were correlated negatively with each other. Besides this, association of reproductive period was significant positive with physiological maturity and significant negative with vegetative duration. Simultaneous selection for capsules per plant and crop maturity duration would serve the purpose of improvement in these traits and yield in sesame. Top yielding isolated lines can be utilized for enhancing yield potential through increasing capsules per plant and earliness.
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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 | 0.000 | 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) | 0.001 | 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".