METALLIC NUTRIENTS IN ENSET (ENSETE VENTRICOSUM) CORM CULTIVATED IN WOLLISO AND WOLKITE TOWNS IN ETHIOPIA
Bibliographic record
Abstract
Enset is an adaptable and drought resistant plant with multiple usages including consumption as co-staple diet in some parts of Ethiopia and has been dubbed “a tree against hunger”. With the plant gaining increased recognition as a food and cash crop, the need for multi-faceted research initiatives appears to be undisputable to preserve its features, maximize its productivity and document changes that would have occurred over the years. The metallic composition of unprocessed corm collected in Wolkite and Wolliso towns in Ethiopia has been investigated. As well as providing information on health benefits or risks, metallic composition may suggest fortification opportunities to improve its nutritive value or that of others and give an insight into temporal alterations. The levels of calcium, magnesium, potassium, iron, zinc, manganese, chromium, cobalt, copper, nickel, cadmium and lead determined with flame atomic absorption spectrometer (FAAS) in digested unprocessed corm samples varied as follows: Ca 36.1–39.1; Mg 24.9–26.9 and K 14.1–32.2 (mg/g); Zn 11.9–42.3; Cu 1.5–5.2; Co 2.8–10.5; Cr 5.8–7.6; Fe 18.2–54.4; Mn 2–5; Ni 1–4 and Cd 0.6–1.8 ( mg/g) with 15.3 μg/g lead being detected in one sample from Wolkite. Method accuracy evaluated as percentage recovery was within 90–110. The levels of metals were higher in samples from Wolliso than those from Wolkite, except for Pb, Mn and Cd. These results indicate that the enset corm, which is low in non-essential nutrients and rich in Ca, Mg, K, Zn and Fe can be recommended as nutritional supplement for deficiencies of Ca, Mg, K, Zn, and Fe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".