Improving Genetic Attributes of Confectionary Traits in Peanut (Arachis hypogaea L.) Using Multivariate Analytical Tools
Bibliographic record
Abstract
Peanut is gaining importance more for its confectionery and nutritive values than for its oil content around the world. Improving confectionery qualities is an added advantage for farming community. Hence, in the present study, multivariate analytical tools were used to identify parents with complementary traits for using them in breeding programme. PCA revealed contribution of pod yield, 100-seed weight, oil content, and O/L ratio towards variance. Pod yield was positively associated with 100-seed weight, oil and protein contents. Oil content had weak association with protein content, oleic acid and O/L ratio. UPGMA clustering revealed grouping of cultivars based on origin and its area recommendation. Cultivars superior for yield (GPBD-4, M-28-2 and JL 24) and confectionery traits (S-230 and Dh-8) were identified. Strong positive relation of yield with confectionery traits indicates possibility of breeding high yielding confectionery grade cultivars. Multivariate analytical tools could be used to identify parents for location specific breeding for improvement of Confectionary traits.
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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 | 0.001 | 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) | 0.000 | 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".