Performance socio-économique du système Agroforestier à <i>Acacia auriculiformis</i> dans la Lama au sud du Bénin
Bibliographic record
Abstract
Au sud du Bénin, et plus particulièrement dans la plantation de la Lama, le manque de terre cultivable constitue un handicap à la survie des populations riveraines. Ainsi, dès 1980, l’administration forestière a intégré dans la gestion de la plantation, le système agroforestier à Acacia auriculiformis dite Taungya. Pour évaluer cette approche, les enquêtes effectuées sont basées sur un échantillonnage des exploitants agricoles à un taux de 10% intégrant le genre. Les petits producteurs (superficie entre 0 et 0,5 ha) sont les plus importants (90%) et utilisent plus une combinaison de la main d’oeuvre familiale et salariée. Cela explique le manque crucial de terre cultivable qui s’observe dans cette zone. Dans ce système, le bénéfice moyen engrangé par période d’exploitation est de 17467 fcfa. Les gros producteurs (superficie supérieure à 1 ha) par contre, n’utilisent pas uniquement la main d’oeuvre familiale et font un bénéfice de plus de 80000 fcfa par période d’exploitation. Ce système permet donc d’assurer un revenu aux populations et par ricochet, permet de palier au problème foncier dans la Lama.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| 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) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".