Embracing Nature: An Optimization Model for Sustainable Tourism Development
Bibliographic record
Abstract
Juneau is a small city with a population of only 32,000, close to Canada. Due to the glacial mountains, the only way to get to Juneau from the rest of Alaska is by boat or airplane. The Mendenhall Glacier, located locally, is a well-known attraction and is visited by a large number of tourists during the cruise season, which runs from early April to the end of October each year. However, the growth of the tourism industry has had negative impacts there, such as the receding glacier and a decline in the quality of life for residents. In order to make the tourism industry in Juneau sustainable, we constructed an optimization model to give suggestions for specific measures. Additionally, we further generalize the model. Among the over-tourism regions, we choose Beijing as the research object. The ecological footprint and sustainable development indicators of the region are considered and measures are proposed to fit Beijing's own situation. Besides, among the regions with few tourists, we choose Qinghai as the target. The data were also processed and analyzed, and the final results show that its tourism industry is declining due to the decrease in the number of tourists. Our suggestion is to promote the natural beauty of the area to attract tourists and thus to see the sustainable development of the tourism industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".