The future of marine fisheries management and conservation in the United Kingdom: Lessons learnt from over 100 years of biased policy
Bibliographic record
Abstract
Marine wild-capture fisheries depend on the capacity of the ocean to provide a flow of harvestable resources to sustain the industry. Paradoxically, conventional fishing often undermines these resources by degrading the environment and overexploiting fish stocks. Many UK fisheries have declined for over a century due to a biased focus on their social-economic value and lack of recognition that they are social-ecological systems and need to be managed as such. With the UK’s recent transition to an independent coastal state, the Fisheries Act (2020) and associated Joint Fisheries Statement provide an opportunity to correct this. Focusing on the ecological foundations, a more sustainable future for UK fisheries may be achieved by: (1) implementing a conservative quota setting system based on Maximum Sustainable Yield (MSY), defined as that which would occur when the biomass of a population of the target species is at 50% of that estimated at carrying capacity, to set catch limits rather than targets. The biomass of fish stocks should be allowed to regenerate to a minimum of 120% of that which will achieve MSY to provide a buffer against the uncertainty in ecological response to climate change. (2) Fishing capacity should be reduced while redistributing a greater share of the quota to sectors of the fleet that are demonstrably more sustainable; recognising that short term compensation may be required by some to mitigate the impacts of displaced activity until the benefits of stock recovery are realised. (3) Greater restrictions should be applied to ensure the most damaging fishing techniques (e.g. bottom trawling and dredging) are prohibited as appropriate in the network of marine protected areas. Protection should be enforced to promote the regeneration of degraded habitats and restoration of fish populations to help achieve the objectives as set out in the Act.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".