Sensitivity of Natural Environment of Tourist Attractions Based on Fuzzy Comprehensive Evaluation
Bibliographic record
Abstract
The study on environmental sensitivity of tourist attractions is an important part of the study on sustainable development of tourism, which is of great significance to the planning, construction, management and sustainable development of tourist attractions.Applying the fuzzy comprehensive evaluation (FCE) method to the evaluation of natural environmental sensitivity of tourist attractions is an attempt to apply the fuzzy mathematics method to the study of environmental sensitivity of tourist attractions.Therefore, this paper proposed to apply the FCE method to evaluate the natural environment sensitivity of scenic spots with rich ecological resources and located in the tourist area.The result showed that among the weight factors of the first-level scenic spots suitable for development, the highest was tourism resources, with a weight value of 0.394.Moreover, no matter what level of scenic spots, when dividing suitable development areas, tourism resources were always the most important factor.Therefore, it is urgent to protect the resources of natural scenic spots.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".