Development of Mangrove Ecotourism in Bandar Bakau Dumai Based on Disaster Mitigation
Bibliographic record
Abstract
Natural disasters that occur in the city of Dumai such as degradation of mangrove forests, coastal abrasion and tidal flooding can be mitigated by maintaining the existence of mangrove forests. Mangrove forests have important benefits on the coast of the city of Dumai, so they need to be protected together. One of the efforts to maintain the existence of mangroves can be through the use of mitigation-based mangrove ecotourism, especially in the Bandar Bakau area of Dumai City. The data collection technique in this study used a quadratic transect and added secondary data from the relevant agencies. Based on the results of the study found 9 types of mangroves that have a role as mitigation in ecotourism locations and there are biota supporting tourism, namely 13 species of birds, 7 species of reptiles and 16 species of molluscs. To maintain the sustainability of the ecotourism area of Bandar Bakau, several disaster mitigations have been carried out for retaining cliffs (revetment), reforestation of mangroves, construction of facilities that adapt to the environment, coastal education, and outreach to the community. In addition, it is very potential to develop several other forms of mitigation such as: beach nourishment, breakwater or construction of embankments to minimize abrasion, as well as construction of diversion canals and tidal flood control gates, strengthening regulations. legislation, making land use policies, policies on flood and wave resistant building standards, policies on exploration and community economic activities, promoting local cultural wisdom of maritime communities.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".