Biodiversity Threats and their Impacts on Eco-tourism in Yabello Wildlife Sanctuary, Southern Ethiopia
Bibliographic record
Abstract
The study on biodiversity threats and their impacts on eco-tourism in Yabello Wildlife Sanctuary were conducted from July, 2013 to September, 2014. Five core wildlife areas were randomly selected to investigate the existing biodiversity threats. A total of 100 villagers, 20 from each core wildlife area were participated in ranking the severity of the biodiversity threats. In addition, 15 protection staff of the sanctuary filled a questionnaire prepared for sowing some management gaps that they encounter. Secondary data like the number of tourist flow and revenue collected was obtained from the head quarter office. Threat indexes were used to analysis the quantitative data and qualitative date were narrated. About nine major biodiversity threats were identified in the sanctuary. The sanctuary is faced by threat factors operating at relatively higher mean relative threat factors severity index (RTFSI) of 0.55 ± 0.01. In addition, insufficient funding and undefined demarcation of the sanctuary are the major management problems affecting conservation of biodiversity. Implications of these on ecotourism activities of the sanctuary are low level of tourist inflow and revenue generated by the sanctuary as well as low benefit accruable to the local economy and the economy of the country as a whole. It was proposed that to reduce the spate of biodiversity threats, conservation awareness aimed at changing local people’s attitude, the provision of essential infrastructural facilities and improvement in peoples’ living conditions should be embarked upon by the management of the sanctuary and government.
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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 | 0.000 |
| 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 | 0.001 |
| 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".