Consequences of fleet diversification in managed and unmanaged fisheries
Bibliographic record
Abstract
Biological diversity is known to play an important role in generating and maintaining ecosystem productivity and other functions and has consequently become a central focus of many efforts to preserve ecosystem services. Theoretical parallels suggest the diversity of fishing fleets may have a similarly important role in determining the productivity and ecological impacts of fisheries, but this possibility has rarely been explored. Here I present theoretical analyses showing that the diversity of métiers — combinations of technology, target species, and fishing grounds — and technical efficiencies in a fishing fleet have important impacts on the productivity, profitability, and ecological impacts of fisheries, particularly mixed-stock or multispecies fisheries. Diversification of métiers can increase yields and reduce threats to weak stocks in both managed and unmanaged multispecies fisheries. Diversification of technical efficiencies creates opportunities for larger profits in managed fisheries, but often decreases yields and worsens impacts on weak stocks in unmanaged fisheries. These results suggest that the potential impact of management may be highest in fisheries with diverse fleets.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".