Hybrid total allowable catch strategy can sustain productive mixed fisheries and conserve both target and non-target species
Bibliographic record
Abstract
Total allowable catches (TACs) are vital for managing fishing pressure and preventing overfishing. However, single-species TACs (SSTACs) in multispecies fisheries often lead to bycatch and choking-species issues, where fisheries close prematurely when the TAC for one species is met. Multispecies TACs (MSTACs), while potentially more effective, are rarely used due to the complexity of multispecies stock assessment. A “hybrid TAC” system, combining SSTAC for target species and MSTAC for non-target species, offers a balanced approach to conserving vulnerable species and managing overall fishing pressure. Using a size-spectrum model for multispecies, multigear fisheries in the Northern Yellow Sea, we evaluated the performance of SSTAC and MSTAC in terms of fishery production, conservation, and ecosystem health. SSTACs reduced target species yield and caused frequent choking-species issues, increasing depletion risks for non-target species. In contrast, MSTACs balanced biomass conservation with yield maintenance, reducing risks at species and community levels. These findings underscore the potential of hybrid TACs in mixed fisheries, emphasizing the need for holistic, flexible management approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".