Threatened species on the menu? Towards sustainable seafood use in zoos and aquariums
Bibliographic record
Abstract
All zoos and aquariums use seafood to a greater or lesser extent, served in restaurants and cafes, and as an important component of animal feed. The majority of their millions of visitors are also seafood consumers. The sustainability of seafood supplies is of major concern: almost 70% of ocean fisheries are either fully exploited or overfished. Approximately 95% of the world's marine production depends on coastal ecosystems, such as estuaries, salt marshes, shallow bays and wetlands, mangroves, coral reefs and sea‐grass beds, which are vulnerable to destructive fishing practices. Food webs have also been severely disrupted. However, fishing is still central to the livelihood and food security of 200 million people, especially in the developing world, with one in five people dependent on fish as their primary source of protein. This paper outlines a number of sustainable seafood initiatives that have been developed by aquariums and conservation organizations. We encourage zoos and aquariums to evaluate their seafood usage, to use available information to inform and increase the sustainability of their consumption, and to encourage changes in the behaviour of their visitors as seafood consumers.
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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 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".