A performance measure framework for ecosystem-based management
Bibliographic record
Abstract
Abstract Effective management of ocean resources is crucial for achieving desired ecological, economic, and social outcomes. Marine ecosystem-based management (EBM) offers a comprehensive approach to achieve these goals, yet its implementation has been challenging and its effectiveness has been unclear. Therefore, we need performance measures to assess the effectiveness of EBM strategies. We developed a semi-quantitative assessment framework using existing indicators and performance measures from the business and project management world (e.g. Key Performance Indicators; KPIs), national and regional economic and social wellbeing performance measures (e.g. GDP, food security), and ecosystem status assessments (e.g. overfishing, biodiversity) to evaluate the success and performance of EBM outcomes. The framework consists of four main categories: (1) sector performance; (2) marine ecosystem status; (3) management and tradeoffs; and (4) human dimensions, each flexible enough to accommodate suitable indicators and reference points. We show how the framework responds to real case studies from Southern New England, the Gulf of Maine, and the Hawaiian Islands, USA; the Baltic Sea; and the Red Sea, Saudi Arabia. The main observation from these performance measures is that higher scores in the management and tradeoffs consideration correlate with higher scores in the marine ecosystem status. Additionally, higher human dimensions scores tend to lead to higher sectoral performance scores. Although it is not certain that one leads to the other, this suggests that EBM is functioning as intended. The framework results show that there are many possible indicators, performance targets, and associated desired directionalities that can be combined to form possible performance measures across combined ocean-use sectors to inform EBM. The challenge lies in using these different operational indicators to assess the strengths and weaknesses of EBM approaches.
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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 | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".