Monitoring of Snapper Fishery Management in Alas Strait Waters, West Nusa Tenggara
Bibliographic record
Abstract
This research examines the sustainability of snapper fisheries in the Alas Strait, West Nusa Tenggara, Indonesia, as part of sustainable fisheries resource management efforts.Utilizing a Multi-Dimensional Scaling (MDS) approach via the Rapid Appraisal for Fisheries (Rapfish) framework, this study evaluated sustainability across five dimensions: ecological, economic, social, institutional, and technological.Primary data were collected through interviews with fishermen, traders, and key stakeholders using structured questionnaires, while secondary data were gathered from relevant government and private institutions.The findings indicate that the overall sustainability index of snapper fisheries in the Alas Strait is 54.21%, categorized as moderately sustainable.The ecological and economic dimensions scored the highest, both at 61.25%, reflecting relatively stable fish stocks and positive economic contributions.Conversely, the institutional and technological dimensions scored the lowest, both below 40%, highlighting significant challenges such as insufficient institutional support, limited access to financial resources, and inadequate adoption of sustainable fishing technologies.Sensitivity analysis identified key attributes influencing sustainability, including fishing gear selectivity, government institutional involvement, and the availability of alternative livelihoods.The results emphasize the importance of addressing these shortcomings through targeted policies and capacity-building programs to enhance the sustainability of the snapper fishery.This study contributes to the understanding of multi-dimensional sustainability and offers actionable recommendations for improving the management of marine resources in Indonesia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".