The Bamendjin Dam and Its Implications in the Upper Noun Valley, Northwest Cameroon
Bibliographic record
Abstract
Understanding the environmental consequences and socio-economic importance of dams is vital in assessing the effects of the Bamendjin dam in the development of agrarian communities in the Upper Noun Valley (UNV) in Northwest Cameroon. The Bamendjin dam drainage basin and its floodplain are endowed with abundant water resources and rich biodiversity, however, poverty is still a dominant factor that accounts for unsustainable management of natural resources by the majority of rural inhabitants in the area. The dam was created in 1975 and has since then exacerbated the environmental conditions and human problems of the region due to lack of flood control during rainy seasons, lost hope of improved navigation system, unclean drinking water sources, population growth, rising unemployment, deteriorating environmental health issues, resettlement problems and land use conflicts, especially farmer-herder conflicts. Despite hopes created by increased production of irrigated swamp rice, introduced to be a major cash crop, socio-economic and ecological problems have significantly reduced its chances of sustainable livelihood and poverty alleviation. Our study addresses the socio-economic implications of the Bamendjin dam as a rural development project to support rice production and other agro-pastoral activities and also examines related rural livelihood problems such as displacement of local communities and transformation of the landscape ecology. Stakeholders need to put in place an institutional framework for decision-making and policy implementation in order to realize the desired benefits of the dam and reverse its adverse effects on the UNV and its environs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".