New Scientific Information Can Help to Inform the Evaluation of EU Deep-sea Fisheries Regulations
Bibliographic record
Abstract
Abstract The European Union’s deep-sea fisheries regulations (Regulation (EU) No. 2016/2336) established obligations to manage deep-sea fisheries and to protect vulnerable marine ecosystems (VMEs). The European Commission is scheduled to complete a review of the regulations in 2021, providing an opportunity for new scientific information to be incorporated into the implementation of the regulations. Here, we summarise research outputs from the EU-funded Horizon 2020 ATLAS Project and explain their relevance to the regulation of deep-sea fisheries in EU waters. ATLAS research has increased our understanding of the distribution of VMEs and their importance in terms of ecosystem functioning. ATLAS research has also highlighted the utility of molecular techniques to understand fish population structure and the potential for habitat suitability models to help incorporate climate change into decision-making. Building on these scientific advances, we provide recommendations to help increase the effectiveness of management measures to conserve deep-sea fish stocks and protect VMEs.
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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 | 0.164 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.036 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".