Analisis Kesesuaian dan Strategi Pengembangan Ekowisata Mangrove di Pulau Penebang, Kecamatan Kepulauan Karimata
Bibliographic record
Abstract
Indonesia has a very long coastline with extensive mangrove forests. Although there is such a vast mangrove forest, only a small portion of it continues to be used as a tourism resource. Efforts should therefore be shaped to make the use of mangroves more valuable, not only from an ecological point of view, but also from a social and economic perspective, in order to increase tourism in the region and contribute to the prosperity of the surrounding communities. And the development of mangrove forest areas for ecotourism indirectly protects the area from damage, whether by nature or by human intervention. The mangrove forests on Penebang Island remain in their natural state, and there is almost no damage from the construction of buildings, so the area still has potential to develop. Mangrove forest areas can be saved in just a few steps and expanded into ecotourism. Analysis using IKW and SWOT analysis shows that this mangrove area is suitable for ecotourism. By local application, residents of Penebang Island can relocate to Pelapis Island or Maya Island and Sukadana. This step was taken because he was the only family member left. Mangrove forest areas can be developed as spatial contributors, with proper planning and proper enforcement by local governments according to their designation, and jointly develop mangrove ecotourism with the participation of investors and local communities. You can. In order to improve the quality of tourism in the region, cooperation among various stakeholders is important in the development of ecotourism on Penebang Island and other tours in the region. The Penebang Island Ecotourism Development Plan can be implemented when the development and development of the area of Pelapis Village is carried out.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".