Total Economic Value of Wetlands Products and Services in Uganda
Bibliographic record
Abstract
Wetlands provide food and non-food products that contribute to income and food security in Uganda. This study determined the economic value of wetland resources and their contribution to food security in the three agroecological zones of Uganda. The values of wetland resources were estimated using primary and secondary data. Market price, Productivity, and Contingent valuation methods were used to estimate the value of wetland resources. The per capita value of fish was approximately US$ 0.49 person⁻¹. Fish spawning was valued at approximately US$ 363,815 year⁻¹, livestock pastures at US$ 4.24 million, domestic water use at US$ 34 million year⁻¹, and the gross annual value added by wetlands to milk production at US$ 1.22 million. Flood control was valued at approximately US$ 1,702,934,880 hectare⁻¹ year⁻¹ and water regulation and recharge at US$ 7,056,360 hectare⁻¹ year⁻¹. Through provision of grass for mulching, wetlands were estimated to contribute to US$ 8.65 million annually. The annual contribution of non-use values was estimated in the range of US$ 7.1 million for water recharge and regulation and to US$ 1.7 billion for flood control. Thus, resource investment for wetlands conservation is economically justified to create incentives for continued benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".