Flexible catch-balancing policies for multispecies individual fishery quotas
Bibliographic record
Abstract
In multispecies fisheries managed with individual fishery quotas, fishers targeting certain species often have insufficient quota to cover other jointly caught species. New Zealand employs a unique, dual quantityprice system to address this problem. In lieu of acquiring quota, fishers can opt to pay a fee per unit of catch known as deemed value (DV). Although designed primarily to create flexibility in catch balancing for individuals, this system can allow aggregate catches to exceed total allowable catches (TACs). The DV system reduces the likelihood that target species catches are constrained by TACs of bycatch species, but also increases the risk of overexploitation of bycatch species. Using a bioeconomic model, we evaluate the risk and efficiency of alternative DV policies in fisheries with one target and one bycatch species. Our simulations suggest that increasing DVs above ex-vessel price in response to TACs being exceeded can control risk of overexploitation without reducing overall efficiency; however, this does shift rents from owners of target species quota to owners of bycatch species quota.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".