Evaluation of Nutrients in Leaves and Seeds of Calotropis Procera (linn)
Bibliographic record
Abstract
Calotropis procera has been widely explored in ethnomedicine to cure several ailments such as leprosy, fever, elephantiasis, menorrhagia, and snakebite. It is also used as a purgative, anthelmintic, anticoagulant, anticancer, antipyretic, analgesic, and carminative. In addition to its traditional use as coagulants, the leaves and seeds of Calotropis procera could be used in food fortifications to combat nutrient deficiencies as reflected in its bioactive components. The increase in its use might be associated with the level of many bioactive components, which provide nutritional and health benefits. Thus, the leaves and seeds were analyzed for their bioactive components and characterized for nutrient values using the procedures of the Association of Official Analytical Chemists. The chemical analyses results showed that the leaf and the seed contained (g/100 g dry weight) moisture (8.11g, 9.53g), crude protein (26.69g, 14.48g), crude fiber (7.54, 15.73), crude fat (21.70, 6.29), ash (5.32, 3.69) and carbohydrate (30.64, 50.29), respectively. The leaves and seeds contained zinc (1.20, 0.60 mg/100 g), potassium (33.60, 30.30 mg/100 g) and iron (36.90, 12.90 mg/100g), respectively. The fatty acids profile revealed that the leaves and the seed oils contained a low level of saturated palmitic acid (3.01, 7.70 g/100g) and a high level of monounsaturated oleic acid (10.31, 27.90 g/100g) and polyunsaturated acids (11.63, 18.53 g/100g), respectively. It is established that the chemical compounds in the Calotropis procera seeds and leaves could be beneficial for therapeutic and dietary purposes. Thus, it can be accepted that the Calotropis procera plant may be used as medicine and food fortificants.
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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 | 0.001 |
| 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".