Marine ecosystem-based management: challenges remain, yet solutions exist, and progress is occurring
Bibliographic record
Abstract
Abstract Marine ecosystem-based management (EBM) is recognized as the best practice for managing multiple ocean-use sectors, explicitly addressing tradeoffs among them. However, implementation is perceived as challenging and often slow. A poll of over 150 international EBM experts revealed progress, challenges, and solutions in EBM implementation worldwide. Subsequent follow-up discussions with over 40 of these experts identified remaining impediments to further implementation of EBM: governance; stakeholder engagement; support; uncertainty about and understanding of EBM; technology and data; communication and marketing. EBM is often portrayed as too complex or too challenging to be fully implemented, but we report that identifiable and achievable solutions exist (e.g., political will, persistence, capacity building, changing incentives, and strategic marketing of EBM), for most of these challenges and some solutions can solve many impediments simultaneously. Furthermore, we are advancing in key components of EBM by practitioners who may not necessarily realize they are doing so under different paradigms. These findings indicate substantial progress on EBM, more than previously reported.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".