Améliorer la compétitivité du bois de sciage légal en provenance de la zone agroforestière au Cameroun
Bibliographic record
Abstract
Since 1997, the year the first community forest in Cameroon was created, sawn wood from community forests has been facing difficulties to find a place in an expanding domestic market. The low competitiveness of community forest products is among the major obstacles to have them help reduce rural poverty as desired in the new forest policies in Central Africa. This article, from the work of the World Wide Fund for Nature team and organizations partners, identifies the factors explaining the low competitiveness of community forests in the domestic market and proposes by way of conclusion some strategies whose implementation will allow community forestry to play its full role in the supply of domestic markets, improving sustainable management of the agroforestry landscape and fostering poverty reduction in rural areas. Key words: wood value chain, artisanal logging, forest policy, community forests
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".