Sea cucumber aquaculture business potential in Middle East and South-East Asia - Pathways for ecological, social and economic sustainability
Bibliographic record
Abstract
Sea cucumbers are delicacy and popular traditional food in South-East Asia especially China, Hong Kong, Singapore, Malaysia and Taiwan. They are also used in traditional Chinese medicine and regarded as a specialty product that falls within the same niche market as other high-value luxury seafood products, including shark fin, fish maw and abalone. Currently more than 50 species of these highly valued bioresources are sold as trepang or Bêche-de-mer, in Asian dried seafood markets. Sea cucumbers are commercially exploited worldwide and the production have expanded in catch and value worldwide during the recent decades.In Middle East region, important sources of sea cucumber are: Egypt, Oman, Yemen, Iran, Saudi Arabia and Turkey while in South-East Asia, Philippines, Indonesia, Malaysia and Vietnam are the main producers with Hong Kong and Singapore being major export countries. Due to overfishing and unsustainable harvest, the supply of sea cucumber is declining throughout the world especially in Middle East and South-East Asia. The increasing demand for the product has kept prices at attractive levels. Aquaculture of sea cucumber is much needed to offset the rapidly declining source, conserve stocks biodiversity and sustain the ecological, social, and economic benefits of these high-valued marine bioresources.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".