Mariculture and Marine Spatial Planning: Integrating Local Ecological Knowledge at Kaledupa Island, Indonesia
Bibliographic record
Abstract
Economic development on Indonesia’s numerous small islands faces a number of challenges stemming from the islands’ isolation and resource limitations. Mariculture has been promoted as a viable development strategy in these areas, and this research assesses a marine spatial planning approach to support net-cage grouper mariculture development in waters surrounding Kaledupa Island located southeast of Sulawesi. Data collection focused on 15 biophysical capability parameters, plus an additional 7 suitability parameters assessed through interviews with villagers and local experts. Capability analysis identified 4,511 hectares capable of sustaining grouper mariculture within the 8,582 hectares study area. Suitability analysis identified 2,667 suitable hectares based on villager opinions and 4,083 suitable hectares based on local expert opinions. Reliance on villager opinions and resolution of fragmentation issues reduced the final area deemed suitable to 2,423 hectares. This study highlights the importance of utilizing local ecological knowledge in marine spatial planning, and emphasizes the need for follow-up studies, monitoring and enforcement of environmental regulations to ensure that negative impacts do not emerge in island communities as a result of mariculture development.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 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".