Minimizing the impact of fishing
Bibliographic record
Abstract
Abstract Minimizing the impact of fishing is an explicit goal in international agreements as well as in regional directives and national laws. To assist in practical implementation, three simple rules for fisheries management are proposed in this study: 1) take less than nature by ensuring that mortality caused by fishing is less than the natural rate of mortality; 2) maintain population sizes above half of natural abundance, at levels where populations are still likely to be able to fulfil their ecosystem functions as prey or predator; and 3) let fish grow and reproduce, by adjusting the size at first capture such that the mean length in the catch equals the length where the biomass of an unexploited cohort would be maximum ( L opt ). For rule 3), the basic equations describing growth in age‐structured populations are re‐examined and a new optimum length for first capture ( L c_opt ) is established. For a given rate of fishing mortality, L c_opt keeps catch and profit near their theoretical optima while maintaining large population sizes. Application of the three rules would not only minimize the impact of fishing on commercial species, it may also achieve several goals of ecosystem‐based fisheries management, such as rebuilding the biomass of prey and predator species in the system and reducing collateral impact of fishing, because with more fish in the water, shorter duration of gear deployment is needed for a given catch. The study also addresses typical criticisms of these common sense rules for fisheries management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".