Genetic Association Analysis and Selection Indices for Yield Attributing Traits in Available Chilli (<i>Capsicum annuum</i> L.) Genotypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it