Nutritious and Nutritional Values of « milks » from Blighia sapida (K.D. Koenig) Arils and Soya Beans (Glycine Max)
Bibliographic record
Abstract
African nations are not self-sufficient in milk and related solutions. So, some fruit, which get nutritious and nutritional value, can be valorized. The aim of this work was to evaluate the effect of Blighia sapida arils "milk" consumption on growth of rats and on the induction of diarrheoa compared to that of soya been "milk". For that, arils "milk" were produced using 0.5 kilogram of arils/ liter of distilled water and soya bean "milk" was brought. The two "milks" were freeze-dried and the powders were used to prepare concentrations (0, 200, 400 and 800 mg / kg of body weight) to be administered every three days to seven groups of rats during 15 days. Every three days, body weight was taken, faeces were collected and their moisture content was determined. Analysis showed that both powders are nutritiously rich. Soya bean "milk" powder protein and lipids content are higher than that of arils "milk" powder but it carbohydrates, calcium and phosphorus content are lower. Both "milks" are in favor of gain weight and they do not induce diarrhoea. So, we can envisage much production of it for human consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".