Ecosystem-based fisheries management: some practical suggestions
Bibliographic record
Abstract
Globally, there is increased scientific and public interest in the concept of ecosystem-based fisheries management (EBFM). This trend is fueled by a widespread perception that large-scale fishing operations are powerful forces altering the structure and function of marine ecosystems. It is acknowledged that management needs to better account for variations in ocean productivity, stock structure, and changing social values. Many countries are contemplating how to improve ocean fishery management. In the United States, fishery management bodies are experiencing pressure to undertake the daunting task of moving from their current single-species management plans to EBFM. Impediments include lack of a clear definition of EBFM, what it entails, or how to proceed. In this paper, characteristics of fishery management that are unique to EBFM are identified. The transition to EBFM needs to be evolutionary rather than revolutionary. A course of action is outlined that can be used to guide this transition. Modeling approaches and metrics useful for planning, implementing, and evaluating EBFM are discussed, with particular emphasis on management strategy evaluation.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".