Exploring balanced harvest as a potential strategy for highly exploited multispecies fisheries
Bibliographic record
Abstract
Abstract Balanced harvest (BH) proposes moderate fishing mortality rates across all species or sizes in proportion to productivity, serving as a possible strategy for ecosystem-based fisheries management. Fishing patterns in some developing countries (e.g. China, the largest producer of seafood) closely resemble BH, where catches have been highly diversified by unselective gears due to market demand for almost all species. In this study, we employed an OSMOSE ecosystem model developed for the Yellow Sea in China to investigate the potential occurrences and advantages of BH in this region with highly exploited multispecies fisheries. Simulations were carried out under four types of fishing scenarios, where various levels of fishing mortality rates for all species or specific functional groups were implemented. Results indicated that the occurrences of BH depended on fishing pressure and targeted functional groups, and that size-level BH was significantly correlated with biomass and yield for most species. In particular, varying fishing pressure for certain functional groups resulted in BH, which produced a high yield for specific species and ensured their biomass sustainability. We concluded that the benefits of BH could be potentially achieved by adjusting fishing pressure for certain functional groups based on the existing fishing pattern in over-exploited ecosystems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".