Individual quotas, fishing effort allocation, and over-quota discarding in mixed fisheries
Bibliographic record
Abstract
Abstract Poos, J. J., Bogaards, J. A., Quirijns, F. J., Gillis, D. M., and Rijnsdorp, A. D. 2010. Individual quotas, fishing effort allocation, and over-quota discarding in mixed fisheries. – ICES Journal of Marine Science, 67: 323–333. Many fisheries are managed by total allowable catches (TACs) and a substantial part by individual quotas. Such output management has not been successful in mixed fisheries when fishers continue to fish while discarding marketable fish. We analyse the effects of individual quotas on spatial and temporal effort allocation and over-quota discarding in a multispecies fishery. Using a spatially explicit dynamic-state variable model, the optimal fishing strategy of fishers constrained by annual individual quotas, facing uncertainty in catch rates, is studied. Individual fishers will move away from areas with high catches of the restricted quota species and, depending on the cost of fishing, will stop fishing in certain periods of the year. Individual vessels will discard marketable fish, but only after their individual quota for the species under consideration has been reached. These results are in line with observations on effort allocation and discarding of marketable fish, both over-quota discarding and highgrading, by the Dutch beam-trawl fleet. The models we present can be used to predict the outcomes of management and are therefore a useful tool for fisheries scientists and managers.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".