Sensorial Characteristics of a Senescent Plantain Empiric Dish (Dockounou) Produced in Côte d’Ivoire
Bibliographic record
Abstract
Dockounou, a plantain derivate dish, is proposed on Côte d’Ivoire markets, under various sensorial qualities. Nevertheless, most of time, consumers’ demands are not satisfied. Hence, a survey was undertaken in Abidjan to determine dockounou consumers’ preferential sensorial criteria, for further improvements. 1250 respondents of both genre, from three age categories (junior, major and senior), literates or not and belonging to all the ethnic groups of Côte d’Ivoire, were interviewed through the whole communes. Among the eight sensorial criteria of dockounou, the majority of respondents identified packaging (98.16%), structure (94.47%), taste (91.11%) and the color (80.13%) as the first essential sensorial criteria for the choice of dockounou. They were followed by the texture (74.29%), flavor (68.43%), cooking mode (66.59%) and the type of flour (59.05%). The specific sensorial characteristics most of the respondents expected, independently to the socio-demographic variables, were Thaumatococcus daniellii leaf (55.59%) as packaging, smooth structure (58.44%), sweet-spiced taste (80.65%), brown color (67.32%), hard texture (57.31%), plantain flavor (73.96%), water cooking (48.08%) and maize (38.24%) and rice (37.09%) flours. However, these sensorial characteristics choices were significantly influenced by the ethnic and the age category more than the genre and the education.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".