Genetic Association Analysis and Selection Indices for Yield Attributing Traits in Available Chilli (<i>Capsicum annuum</i> L.) Genotypes
Bibliographic record
Abstract
The present investigation was conducted with 30 chilli genotypes at the experimental field of Regional Spices Research Centre, BARI, Gazipur to assess the genetic association and selection indices among yield and important yield attributing traits. Fruit length, fruit weight, 100 seed weight and fruits/plant showed significant and positive correlation with yield/plant both at genotypic and phenotypic level. Path coefficient analysis revealed that fruits/plant had maximum positive direct effect on yield. Besides fruits/plant; fruit weight, fruit length and number of primary branches/plant also contributed positive direct effect to yield. Selection indices were constructed through the discriminate functions using five characters. Highest relative efficiency was found for fruit weight +fruits/plant +yield/plant comparable to other combinations of characters. This research indicated that the selection of high yielding chilli genotypes based on these three characters might be more efficient. Biplot analysis was also performed to find out superior genotypes.
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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 | 0.001 |
| 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".