Evaluating the decision on choosing fishing ground for artisanal fisheries using spatial and Fuzzy DEMATEL in small islands: Sustainability driven
Bibliographic record
Abstract
The maintenance of a sustainable mechanism to curb the rate of overfishing in similar water zones is one of the major challenges facing artisan fisheries in small island regions. Several factors instigate the decision by local fishermen to potentially choose a location to explore. Obviously, the demand to address these triggers, particularly those with negative impact tendencies, has become paramount. This study, therefore, is aimed at mapping out fishing heat spots based on arrival, background of the fishermen, and available species. Subsequently, an evaluation is conducted to ascertain the criteria influencing the decision on a specific position and also to determine the consequences of their negative impact on marine resource sustainability and coastal community welfare. For these reasons, data from fishermen, sellers and local distributors were collected by using questionnaires, and analyzed to generate useful information on related fishing ground. Furthermore, experts were involved in providing expert assessment for Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The results showed significant insights and therefore offer support to help regulators preserve marine resources, especially by updating their decision making capacity for increased coastal community welfare. The direct implication of this research also serves as a basis for conducting traditional fisheries, estimated not only to meet economic impact, but also promote sustainable environment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".