Balanced harvesting in fisheries: a preliminary analysis of management implications‡
Bibliographic record
Abstract
Abstract Balanced harvest (BH) proposes to distribute a moderate mortality from fishing across the widest possible range of species, stocks, and sizes in an ecosystem, in proportion to their natural productivity so that the relative size and species composition are maintained, in line with the CBD requirement for sustainable use. This proposal has many and not always intuitive implications for fisheries management, e.g. in relation to selectivity, protection of juveniles and spawning sites, models of harvesting strategies, a focus on size and species, the impacts of discarding, aspects of emblematic species and ecosystem services, operational complexity, partial implementation, ecosystem rebuilding, and relations with broader management frameworks. The paper closes with a discussion of BH implementation, concluding that a logical step would be to integrate several separate initiatives to move fisheries into a more ecosystem-conscious context. Implementation challenges will be encountered, but there are lessons to be drawn from fishery ecosystems already close to BH, as in some tropical multispecies fisheries, and further, the implementation challenges are already being taken on in many well-managed fisheries and areas as management begins to address the realities of what ecosystem-based fishery management actually entails.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".