AN ATTEMPT TO IDENTIFY CULTURAL ECOSYSTEM SERVICES AND RELATED LAND USE TYPES IN RURAL AREAS UNDER URBANIZATION
Bibliographic record
Abstract
Cultural Ecosystem Services (CES) play an important role in socio-natural ecosystems. Assessment of CES in rural areas is crucial for development planning and decision-making. However, assessment of CES at the local scale and, in particular, rural areas remain under-researched. In order to reveal the importance of different kinds of CES and the related land uses perceived by the rural residents, a simplified tick-scoring method was developed and tested in a case study of four villages in Shandong Province, China. This method poses CES questions and seeks answers about corresponding land use types in a questionnaire form that is accessible and useful to village residents. Furthermore, the important categories of CES and related land use types were identified and ranked based on the questionnaire. The results showed that ecological culture and aesthetic services ranked in the top two of twelve CES categories, while scenic spots/mountains, forests, and lakes/rivers/reservoirs scored for multiple CES and attained higher than average CES scores. Overall, the simplified method is practical to understand the perspectives of rural residents on the important CES and related landscapes. The established approach shed lights on CES assessment and management improvement at local scale of rural areas under different socio-environmental contexts in China and elsewhere.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".