Case studies demonstrate capacity for a structured planning process for ecosystem-based fisheries management
Bibliographic record
Abstract
Structured, systematic processes for decision-making can facilitate implementation of ecosystem-based fisheries management (EBFM). In US fisheries management, existing fishery ecosystem plans (FEPs) are primarily descriptive documents — not action-oriented planning processes. “Next-generation” FEPs extend existing FEPs by translating ecosystem principles into action through a structured process, including identifying and prioritizing objectives and evaluating trade-offs while assessing alternative management strategies for meeting objectives. We illustrate the potential for implementing a structured decision-making process for EBFM by reviewing fisheries management case studies through the lens of the next-generation FEP process, highlighting two perspectives. First, across case studies almost all steps occur, many occurring in multiple regions, indicating scientific and fisheries management capacity exists to conduct structured process components. Second, adjustments would be needed to transition to next-generation FEPs, as existing activity is rarely conducted within a fully structured, integrated process and examples of certain steps are scarce, but existing examples can guide future management. Implementing ongoing activity within next-generation FEPs would likely streamline fisheries management activity, saving time and resources while improving outcomes for stakeholders and 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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".