An Analysis of the Environmental Vulnerability Index of a Small Island: Lipe Island, Kho Sarai Sub-District, Mueang District, Satun Province, Thailand
Bibliographic record
Abstract
Thailand is located in South East Asia and is a popular tourist destination. It is rich in both natural resources and culture. There are 691 islands in Thailand, and more than 214 of these islands are used for tourism. Koh Lipe is very Small Island of approximately 2 square kilometers, located in Talutao National Park in the southern part of Thailand. This research aims to assess the sensitivity of the Island in terms of tourism development by using the Environmental Vulnerability Index, or EVI. The results showed that the EVI of Lipe Island is approximately 5.7, which represents a very high vulnerability score. Particularly, the REI, the level of risk to hazard, which measures influences on the environment within the island (e.g., loss of forestry, tourist accommodation, waste water and solid waste) was approximately 6.2, while the EDI, the natural resilience of the state based on its native characteristics, (e.g. water resources, protected area, marine protected area, and law enforcement), was approximately 5.7. This is also a very important indicator of the vulnerability of the Island. Thus, to reduce the overall vulnerability of the island, all indicators included in the REI and the EDI must become management priorities. Over time, this will increase the immunity of the island to of the impact of tourism development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".