Production, Marketing and Biochemical Characterization of Gnonmi (Millet Fritter) Consumed in Korhogo in Cote d’Ivoire
Bibliographic record
Abstract
Gnonmi is a widely consumed food in Cote d’Ivoire and originates from the Northern part. Originally, it is mainly made from millet, but a variety of cereals are used for its cooking. The aim of this study was to generate more knowledge on the methods of preparation of gnonmi and marketing as well as its nutritional characteristics. A cluster and snowball survey were carried out in different neighborhoods of the city of Korhogo. Then, some samples of gnonmi were subjected to biochemical analyses. Based on the results obtained, 19 sellers surveyed were between 18 and 60 years old and 52% were not in school. At the production level, the cereals used were millet, raw and cooked rice and corn. These cereals were used in varying proportions. Gnonmi production is an income-generating activity for the women of Korhogo and a source of nutrient intake for the population. Physicochemical analysis showed that Gnonmi marketed in the city of Korhogo had an average pH of 4.13 for a titratable acidity of 0.42 meq/100g, a dry matter content of 65.8% and a water content of 34.2%. On average, the dietary fiber, ash, carbohydrate, protein and lipid levels were respectively 30.44%; 0.51%; 39.62%; 5.22% and 20.42% for an energy value of 363 Kcal/100g. Knowledge of biochemical parameters facilitates the prospects for improving and formulating foods richer in nutrients for the well-being of consumers
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".