Conserving wild fish in a sea of market-based efforts
Bibliographic record
Abstract
Abstract Over the past decade conservation groups have put considerable effort into educating consumers and changing patterns of household consumption. Many groups aiming to reduce overfishing and encourage sustainable fishing practices have turned to new market-based tools, including consumer awareness campaigns and seafood certification schemes (e.g. the Marine Stewardship Council) that have been well received by the fishing and fish marketing industries and by the public in many western countries. Here, we review difficulties that may impede further progress, such as consumer confusion, lack of traceability and a lack of demonstrably improved conservation status for the fish that are meant to be protected. Despite these issues, market-based initiatives may have a place in fisheries conservation in raising awareness among consumers and in encouraging suppliers to adopt better practices. We also present several additional avenues for market-based conservation measures that may strengthen or complement current initiatives, such as working higher in the demand chain, connecting seafood security to climate change via life cycle analysis, diverting small fish away from the fishmeal industry into human food markets, and the elimination of fisheries subsidies. Finally, as was done with greenhouse gas emissions, scientists, conservation groups and governments should set seafood consumption targets.
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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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".