‘Horses for courses’ – an interrogation of tools for marine ecosystem-based management
Bibliographic record
Abstract
Marine Ecosystem-Based Management (EBM) approaches are a well-established and fundamental component of international agreements and treaties, regional seas conventions, assessment strategies, European Directives and national and regional instruments. However, there is the need to interrogate and clarify the implementation of EBM approaches under current marine management. Although particular focus here is within the European Union Marine Strategy Framework Directive (MSFD), all lessons learned are applicable to marine assessments and management in seas worldwide given that all marine management instruments aim to ensure sustainability in marine ecosystems and human uses. Notably, the MSFD aims to ensure that Good Environmental Status (GES) will be achieved thereby enabling the sustainability of coastal and marine ecosystems to deliver ecosystem services and societal goods and benefits while at the same time being adaptive to rapid climate and environmental changes. As a clear understanding of EBM and the tools available to achieve it is needed for practitioners, regulators and their advisors, the analysis here firstly presents the current understanding of EBM (including its origin and application) and the wider 26 principles on which it is based. Secondly, we identify the key elements that are addressed by those principles (18 key EBM elements). Thirdly, we identify the types of tools available for use in the EBM context (19 tool groups). Fourthly we analyze the suitability of tool types to deliver the key EBM elements using an expert judgement approach. Finally, we conclude with the lessons learned from the use of those tools and briefly indicate how they could be combined to help achieve EBM in the most effective way. It is emphasized that no single tool is likely to satisfy all aspects of EBM and therefore employing a complementary suite of tools as part of a toolbox is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".