Genotype Effect on Proximate and Mineral Analysis of Safflower as a Green Leafy Vegetable
Bibliographic record
Abstract
Two field trials were carried out at the Botswana University of Agriculture and Natural Resources Content Farm (59o24′S, 95o25′E and 993 m above sea level) in Southern Region of Botswana, to evaluate the effects of genotypes on proximate and mineral composition of safflower leaves. Five safflower genotypes (Kiama composite (local), Sina-PI-537598, Gila-PI-537692, PI-537636 and PI-527710) were evaluated in a randomized complete block design (RCBD) with three replications. During the rosette stage safflower leaves (both petiole and blade) were harvested, dried, and ground for analysis. The results showed that safflower genotypes significantly varied in the leaf proximate content of crude protein (24-28%), crude fibre (8-14%), moisture content (86-87%) and dry matter content (13-14%) in both winter and summer growing seasons. The genotype ‘Sina’ had the highest crude fibre content compared to other genotypes. The average leaf mineral content significantly (p < 0.05) varied from 2-3 mg g-1 phosphorus, 3-4 mg g-1 calcium, 5-6 mg g-1 sodium, 15-17 mg g-1 magnesium and 15-18 mg g-1 potassium. The Na:K and Ca:P ratios ranged between 0.18 and 3.41. All the safflower genotypes evaluated had sufficient nutritional content to be used as a green leafy vegetable for human consumption and food security.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".