Towards an ecosystem approach to fisheries in Europe: a perspective on existing progress and future directions
Bibliographic record
Abstract
Abstract European countries are committed to adopting an ecosystem approach. These commitments are expected to influence the forthcoming reform of the Common Fisheries Policy (CFP) and thus progress towards implementing an ecosystem approach to fisheries (EAF). We suggest that the major impediments to adopting an EAF in the current CFP are the low specificity and incompatibility of environmental, social and economic objectives, no agreed guidance on the priority to be given to objectives when trade‐offs have to be made, a decision‐making system that is strongly influenced by short‐term national interests, and low societal and political will to pay the high short‐term costs of making the transition to sustainability. Notwithstanding, a high‐level policy framework to support the ecosystem approach and EAF, along with many of the tools needed to support an operational system, is now emerging. The rate of progress towards an operational EAF following CFP reform will be influenced by the requirement for a reformed management system to help achieve Good Environmental Status for ecosystem components and attributes, in support of European policy for the marine environment (principally the Marine Strategy Framework Directive). However, the willingness and capacity of member states to meet high short‐term transition costs, the extent of their support for transferring tactical decision‐making to regional bodies and their views on acceptable transition rates are likely to have more influence on progress towards meeting the objectives of an EAF.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".