WTO must complete an ambitious fisheries subsidies agreement
Bibliographic record
Abstract
The World Trade Organization (WTO) achieved a significant milestone in June 2022 by adopting a much-anticipated fisheries subsidies agreement 1 , aligning with strong recommendation from the global scientific community 2 . This pivotal agreement marks a crucial advance towards ensuring the sustainability of our ocean. For the first time, it establishes binding global regulations compelling governments to assess the legality and sustainability of the fishing activities they subsidize. Harmful subsidies are a key driver of overfishing which is a major threat to ocean biodiversity 3 . Subsidies also exacerbate CO 2 emissions from fishing sectors by incentivizing over-capacity 4 and putting coastal livelihoods and food security at risk 5 . Within this agreement, trade ministers committed to further negotiations on unresolved matters. Such matters include crafting new regulations to diminish subsidies contributing to overfishing and excessive fishing capacity (Fig. 1 ) that have given some countries an unfair advantage in exploiting the ocean 6 . Removing harmful subsidies and therefore overfishing, will help to rebuild diverse fish populations, subsequently leading to increased levels of sustainable catches, and income for fishers. Rebuilt fish populations would also help reduce carbon emissions 7 , 8 . Fig. 1 Fisheries subsidies amount by category and type and grouped by developed and developing country groups (dark vs. light blue, respectively), for 2018 6 . Full size image
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".