Mapping Cultural Ecosystem Services in Different Landscapes through the Perception of Tourists in Ugam Chatkal National Nature Park, Uzbekistan
Bibliographic record
Abstract
Landscapes provide many ecosystem services, such as food and fibers, carbon sequestration, recreation possibilities, and aesthetic beauty or spirituality. These latter three services are cultural ecosystem services, which are rarely studied and their spatial distribution is poorly known. I developed and applied a framework to classify and map the provision of cultural ecosystem services as experienced by tourists in the Ugam Chatkal National Nature Park, which is located in the Uzbek Tashkent region. In this study, a photo-based questionnaire survey is combined with cartographic images of different landscape types to obtain hot and cold spot areas of cultural ecosystem services. The tourists’ sociodemographic backgrounds on how they perceive these services are statistically analyzed. Each cultural ecosystem service shows a distinct spatial pattern in its distribution and in the different landscapes (i.e. natural lakes, traditional meadows, and forests) in which they occur. Specifically, midlands landscapes between 1,200 masl and 3,500 masl are considered as hotspot areas for recreational activities, aesthetic beauty, and spirituality. The highland zones above 3,500 masl mainly provide cultural heritage and recreational activities. The lowland plains below 1,200 masl do not provide major services. My results demonstrate that the tourist perception is most influenced by nationality and degree of education. Other factors, such as gender, age, and environmental behavior have a less importance in defining tourists’ perceptions.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".