Assessment of Genetic Divergence in among Yard Long Bean (<i>Vigna unguiculata</i> subsp. <i>sesquipedalis</i> [L.]) Genotypes
Bibliographic record
Abstract
Forty four genotypes of yard long bean ( Vigna unguiculata subsp. sesquipedalis (L.) Verd.) were investigated to understand the extent of genetic diversity through twelve quantitative traits. Mahalanobis’s D 2 analysis established the presence of wide genetic diversity among these genotypes by the formation of 3 clusters. Cluster I had the maximum number of genotypes i.e 34 and cluster III had only four genotypes. Intra cluster distance analysis revealed that the minimum intra cluster distance was observed in the cluster I. The inter-cluster distance (D) was found to be the maximum between the clusters II and III and the same was minimum between clusters I and II. The results indicated that 100 seed weight contributed maximum to the total divergence followed by pod yield per plant. Intercrossing among the genotypes belonging to cluster II, V and IV was suggested to develop high yielding varieties with other desirable characters or may be used as potential donors for future hybridization programme to develop superior yard long bean variety with good consumer preference and high pod yield.
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.
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".