Influence of trends in fishing power on bioeconomics in the North Sea flatfish fishery regulated by catches or by effort quotas
Bibliographic record
Abstract
Total allowable effort quotas (TAEs) are often considered as promising alternatives to single-species total allowable catch quotas (TACs) in fisheries management. However, implementing TAEs would primarily require converting nominal effort into fishing mortality rate, i.e., developing a relevant model of the dynamics of catchability. Assuming that trends in the catchability may occur through fishing-power creeping linked to increased capital invested and technology, we compare, by stochastic and dynamic simulation modelling, the relative biological and economic benefits and drawbacks of TAEs and TACs in the North Sea sole (Solea solea L.) and plaice (Pleuronectes platessa L.) fishery. Management targets are based on the Precautionary Approach and Harvest Control Rules developed by the International Council for the Exploration of the Sea (ICES). Fishermen are assumed to set their effort at the most (TAE) or least (TAC) conservative level. Overall, the outcomes are more sensitive to the catchability model when implementing TAEs, especially in the medium and long term, but the variability in catchability has a much greater impact on stock levels than on the fisheries profit.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".