Revered and Reviled: The Plight of the Vanishing Sea Cucumbers
Bibliographic record
Abstract
Sea cucumbers paradoxically suffer from being both highly prized and commonly disregarded. As an Asian medicine and delicacy, they command fabulous prices and are thus overfished, poached, and trafficked. As noncharismatic animals, many are understudied and inadequately protected. Despite presenting a rich diversity of life histories, members of this broad taxonomic group (class Holothuroidea) are often managed simply as "sea cucumbers" in fisheries worldwide. One cannot imagine fishes (class Pisces) being given the same universal treatment. Yet this may happen for species of sea cucumber that differ on the same fundamental level as tilapia and tuna. As more sea cucumbers reach an endangered status and wild populations become depleted to the point of collapse, critical questions arise about the relevance of established conservation and governance strategies. This article reviews the main threats faced by exploited sea cucumbers, outlines conservation and governance effectiveness, identifies gaps in knowledge, and explores management and research perspectives in the context of climate change and booming fisheries crime. We stress the perilous state of harvested sea cucumbers globally and the urgent need for action.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".