Supply chain pattern of blue swimming crabs in the north coast of Java, Indonesia
Bibliographic record
Abstract
The blue swimming crabs (BSC) fishing industry in Indonesia is heading towards an imbalance between demand and sustainability of its resources, a factor in supply. The mismanagement of traceability data and information in the supply chain is suspected to be one of the causes adding to the complexity of recent BSC fisheries management. This research aimed to identify BSC supply chain patterns and their issues, especially on two coastal regencies on the North Coast of the Java Sea. The research was conducted during June and December 2023 in Cirebon Regency and Rembang Regency. The study enlisted 70 participants, comprising fishermen, traders, processors, and exporters, with data acquisition facilitated through surveys, interviews, and focus group discussions. Respondent selection employed purposive sampling, while snowball sampling identified pertinent informants within the supply chain. Data analysis encompassed qualitative description and Likert-type scale perception analysis. The findings show that the BSC fishing industry on the North Coast of the Java Sea operates as small-scale fisheries (SSF), with unique vessel types, gear, and socioeconomic conditions crucial for livelihoods. Fishermen prioritize sustainability by using small vessels and traditional gear despite limited education. The BSC supply chain involves stakeholders offering high-value products, yet by-product utilization potential is untapped, requiring improved coordination and innovation. Challenges include export standards, market fluctuations, and product safety, addressed through regulatory support and collaboration. Government regulations, fishermen groups, and data traceability enhance market transparency and sustainability. Therefore, collective action and innovative approaches are vital for long-term economic prosperity and environmental stewardship in the BSC supply chain.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".