Sustainable "Seafood" Ecolabeling and Awareness Initiatives in the Context of Inland Fisheries: Increasing Food Security and Protecting Ecosystems
Bibliographic record
Abstract
The sustainable seafood movement has adopted a variety of certification and ecolabeling systems, as well as seafood-awareness campaigns, to influence industry and help consumers make informed decisions regarding their seafood consumption. However, a review of these programs revealed that the majority are focused on marine and coastal fisheries. Globally, freshwaters and their fish assemblages represent some of the most threatened systems and taxa because of multiple anthropogenic stressors. There is an urgent need to harness the momentum of the sustainable seafood movement for marine systems to benefit all aquatic systems, including freshwater. Moreover, given that freshwater systems are at particular risk in developing countries in which small-scale fisheries dominate, it is essential to expand awareness campaigns, through grassroots initiatives that differ significantly from current awareness campaigns that are global in focus, involve industrialized large-scale fisheries, and assume significant exports of seafood. Addressing the limitations of marine campaigns is a logical first step before launching new programs aimed at inland fisheries. In the long term, failure of the sustainable seafood movement to incorporate freshwater fisheries will lead to public perception that these fisheries are not in peril and may allow unsustainable practices to continue.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".