Review on Pumkin Production and Nutritional Value in Ethiopia
Bibliographic record
Abstract
Among cucurbitaceous vegetables, pumpkin has been appreciated for high yields, long storage life and high nutritive value.All world continent produce pumpkin except Antarctica: including United States, Canada, Mexico, India, and China.Large number of pumpkin varieties varying in shape, size and colour of flesh are available.Cucurbita pepo, Cucurbita maxima and Cucurbita moschata are the worldwide commonly grown species of pumpkin.The weather of Ethiopia makes a suitable environment for the growth of pumpkin.Few years ago, farmers used to produce pumpkin in their gardens together with cereals, in farms near fences for the plants to easily creep on fences and houses, marginal or waste land, on decaying hay and heap of cow dung.It is commonly known to be used for both food and in herbal medicine formulation for the treatment of various ailments.Pumpkin contains biologically active compounds like polysaccharides, para-aminobenzoic acid, fixed oils, sterol, proteins and peptides.The fruits are a good source of carotenoid and γ -aminobutyric acid.Pumpkin seeds are good sources of protein, fats, carbohydrates and minerals.It said to have contained 93% essential amino acids, 53% crude fat and 27% crude protein.The seed contains oil which is used for cooking.Pumpkin despite its enormous benefits, information on production, challenges and nutritive value was limited in Ethiopia.This is as a result of inadequate knowledge on its importance and how livelihoods of many families will be affected by its production.Therefore the objective of this paper was to review the production status and challenges and nutritive values of pumpkin in Ethiopia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".