Effects of Peri-Urban Land Use Changes on Forest Ecosystem Services: The Case of Settlements Surrounding Pugu and Kazimzumbwi Forest Reserves in Tanzania
Bibliographic record
Abstract
Peri-urban settlements constitute foci of urban expansion in most cities of the developing world. They provide livelihood opportunities by exploiting adjacent resources such as forest products, land and water. Yet they constitute a conflicting zone of development whereby urban and rural livelihoods compete for space. This paper examines the effects of peri-urban land-use changes on forest ecosystem services from Pugu and Kazimzumbwi forest reserves. Land use changes were analysed using series aerial photographs of between 1975 and 2012. This was complemented with participatory resource mapping, focus group discussion and key informant interviews to identify and qualify changes in ecosystem services over the period of 37 years. Literature review was also used to capture non-spatial data. Results indicate that there has been tremendous change in built-up area surrounding the forest reserve. It increased from 608.78 hectares in 1975 to 4,933.51 hectares in 2012 representing an increase from 2.4 to 19.1 percent. The same trend pervaded residential and the reverse for open land uses. These changes have resulted into disappearance of ecosystem services (plant and animal species, honey and wax production, mushrooms and water resources). The remaining forest ecosystem services are likely to completely disappear in few years if protection and conservation measures will not be stepped up. Guided land use plans for all areas surrounding the forest reserves and decentralized forest management have been recommended to facilitate restoration of forest services.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".