Diagnosis of Millet [Pennisetum glaucum (L.) R. Br (L.)] Cultivation Practices in Côte d’Ivoire and Study of the Morphological Diversity of Millet Ears Found in Cultivation Areas
Bibliographic record
Abstract
The lack of improved varieties and the decline in millet cultivation in certain regions have led to genetic erosion and a drop in production. This study was conducted to examine various aspects of millet production, highlighting the social, cultural, and agronomic dynamics that influence this crop, and then to characterize the millet ears present in production areas. The results reveal a high prevalence of millet cultivation by men (88%) than women (12%), despite an earlier tradition in which it was mainly associated to women. There is great ethnic diversity among farmers, with agricultural practices and crop preferences varying from one ethnic group to another. The use of agricultural inputs, mainly mineral fertilizers (60%), is widespread, although differences in yields between genders, highlight disparities in farm management (1,420 kg/ha for men vs 745 kg/ha for women). Constraints such as pests, climatic conditions and soil quality are reported as major challenges for millet production. Analysis of the morphological diversity of millet ears revealed a high degree of morphological variability, with four distinct classes identified, pointing the potential for breeding adapted varieties.
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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 | 0.002 | 0.001 |
| 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".