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 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 | 0.001 |
| 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".