Matching catches to quotas in a multispecies trawl fishery: targeting and avoidance behavior under individual transferable quotas
Bibliographic record
Abstract
Optimizing yield in multispecies fisheries is only possible when fishers have a high degree of control over the species mixture in their catches, although incentives to encourage this kind of behavior are rarely in place. One exception is the British Columbia, Canada, groundfish trawl fishery, where individual transferable quotas govern total allowable catches (TACs) for 22 species, combined with 100% observer coverage and the deduction of discard mortality from quota. Despite the number of species covered, when TACs were increased for some species and reduced for others, fishers were able to adjust the species mixture in their catches. The top 34 vessels frequented a wide range (mean 38, range 20–69) of “fishing opportunities” (repeated trawls along the same track line) containing predictable species mixtures. When TACs for rougheye ( Sebastes aleutianus ), shortraker ( Sebastes borealis ), and yelloweye rockfish ( Sebastes ruberrimus ) were sharply cut, fishers avoided fishing opportunities where these species were more abundant. More generally, their choice of fishing opportunities depended on the expected multispecies composition, although other factors were probably also important.
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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) | 0.000 | 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 | 0.001 |
| 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 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".