MORPHOLOGICAL CHARACTERIZATION AND MULTIVARIATE ANALYSIS OF DIFFERENT GENOTYPES OF MUNGBEAN (VIGNA RADIATA (L.) R. WILCZEK)
Bibliographic record
Abstract
Mungbean (Vigna radiata (L.) R. Wilczek) is versatile crop mainly grown in subtropical regions with wide-ranging agricultural and nutritional benefits. Despite being the largest producer in the world, the productivity of mungbean is well below in India due to limited morphological variability observed in working collection of mungbean. In the present investigation, we have conducted morphological characterization and multivariate analysis to explore the genetic diversity among the 26 genotypes of mungbean (Vigna radiata (L.) R. Wilczek). These 26 accessions of mungbean procured from the Pulses Research Station, SDAU, Gujarat, and local markets were grown for evaluation in Randomized Block Design (RBD) with three replications during June to August 2021. Morphological observations recorded at different stages of life for the grown genotypes. Significant variations observed in morphological traits of different genotypes. Principal component analysis and cluster analysis discriminated following genotypes such as VM, GM-6, SKNM-1608, SKNM-1701, SKNM-1704, SKNM-1801, SKNM-1802, SKNM-1806, and SKNM-1808 based on the morphological observations noted above. These genotypes can be recommended to use as parent for further plant breeding programmes to develop new variety and conduct field trials for different locations and climates.
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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 | 0.001 | 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) | 0.001 | 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".