Sea cucumbers in a pickle: the economic geography of the serial exploitation of sea cucumbers
Bibliographic record
Abstract
Serial exploitation comprises a pattern of the human exploitation of wild harvest fisheries, where previously untapped species or locations come under exploitation over both space and time.Unless managed sustainably, serial exploitation can lead to serial depletion of local fisheries, thereby adversely affecting local ecosystems, economies, and communities.Serial depletion is an archetypal problem of the Anthropocene, as its occurrence depends on trade linkages between consumers in one location and suppliers from sometimes geographically very distant fisheries.Invertebrates, especially echinoderms such as sea cucumbers, are subject to serial exploitation that is occurring now on a global scale.We found that the serial depletion of sea cucumbers was consistent with variability in the global mean price for sea cucumbers.When local fisheries are depleted, price tends to rise; a rising price signals previously unexploited fisheries to begin supplying the market.This cycle repeats itself, spreading from the regional to the global scale.Improved understanding of what drives serial exploitation may allow for more successful management of sea cucumber fisheries in the future.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".