Evaluation and Identification of Genetic Variation Pattern in Cowpea [Vigna unguiculata (L.) Walp] Accessions Using Multivariate Analyses
Bibliographic record
Abstract
Thirty accessions of cowpea from Ebonyi, Enugu and Kogi States, Nigeria were used for the study. Seeds of the accessions were randomized in a plot measuring 50x50 meters for three growing seasons. Our results on yield and yield-related traits showed that three principal components were extracted, which contributed 82.23% of the total variability. It revealed that number of seeds pod-1, 100-seed weight, pod length, days to 50% maturity, seed yield, number of leaves plant-1 contributed significantly to the total genetic variability while for proximate composition, four principal components accounted for 93.75% of the total genetic divergence. Cluster analysis revealed that accessions were grouped not necessarily based on geographical location but genetics. Selection for high yielding accessions should be done on cluster 2 as we recommend selection and hybridization of accessions from cluster 1, 2, and 3 for optimal benefit.
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How this classification was reachedexpand
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 | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".